# People cannot distinguish GPT-4 from a human in a Turing test

Cameron R. Jones
Department of Cognitive Science
UC San Diego
San Diego, CA 92119
<EMAIL_ADDRESS>
Benjamin K. Bergen
Department of Cognitive Science
UC San Diego
San Diego, CA 92119
<EMAIL_ADDRESS>

###### Abstract

We evaluated 3 systems (ELIZA, GPT-3.5 and GPT-4) in a randomized, controlled,
and preregistered Turing test. Human participants had a 5 minute conversation
with either a human or an AI, and judged whether or not they thought their
interlocutor was human. GPT-4 was judged to be a human 54% of the time,
outperforming ELIZA (22%) but lagging behind actual humans (67%). The results
provide the first robust empirical demonstration that any artificial system
passes an interactive 2-player Turing test. The results have implications for
debates around machine intelligence and, more urgently, suggest that deception
by current AI systems may go undetected. Analysis of participants’ strategies
and reasoning suggests that stylistic and socio-emotional factors play a
larger role in passing the Turing test than traditional notions of
intelligence.

##  1 Introduction

###  1.1 The Turing test

Progress in artificial intelligence has led to systems that behave in
strikingly humanlike ways. Large Language Models like GPT-4 (OpenAI, 2023) not
only produce fluent, naturalistic text, but also perform at parity with humans
on a range of language-based tasks (Chang and Bergen, 2024). These systems are
increasingly being deployed to interact with people on the internet, from
providing assistance as customer service agents (Soni, 2023) to spreading
misinformation on social media (Zellers et al., 2019; Park et al., 2023). As a
result, people interacting anonymously online are increasingly forced to ask
themselves the question: “Am I speaking to a human or a machine right now?"

Unwittingly, these people are engaging in a real-world analogue of a thought
experiment dreamed up three quarters of a century ago by the computer
scientist and mathematician Alan Turing. In his seminal article, Turing (1950)
proposed a test to measure whether a machine could generate behaviour that was
indistinguishable from a human. In his original formulation—which he referred
to as the imitation game—a human interrogator would speak to two witnesses
(one human and one machine) via a text-only interface. If the interrogator was
not able to reliably distinguish between the human and the machine, the
machine would be said to have passed (French, 2000).

Turing’s article “has unquestionably generated more commentary and controversy
than any other article in the field of artificial intelligence” (French, 2000)
(p. 116). Turing originally envisioned the test as a measure of machine
intelligence; if a machine could imitate human behaviour on the gamut of
topics available in natural language—from logic to love—on what grounds could
we argue that the human is intelligent but the machine is not? However, this
idea has accrued a raft of objections in the intervening years, for instance
that the test is too easy (Marcus et al., 2016; Gunderson, 1964), or too hard
(Saygin et al., 2000), or too chauvinistic (French, 2000): a controversy that
we return to in the discussion.

Independent of intelligence, the Turing test at its core probes something
potentially more urgent—whether people can tell when they are communicating
with a machine. Systems that can robustly masquerade as humans could have
widespread social and economic consequences (Frey and Osborne, 2017; Zellers
et al., 2019; Ngo et al., 2023). The Turing test also serves as a window onto
our own conceptions of what it is to be human (Hayes and Ford, 1995; Turkle,
2011). As interrogators devise and refine questions, they implicitly reveal
their assumptions about what makes humans unique, and which qualities would be
hardest to imitate.

Over the last 74 years there have been many attempts to implement Turing
tests, though few have been controlled experiments (Oppy and Dowe, 2021). The
Loebner Prize (Shieber, 1994)—an annual competition in which entrant systems
tried to fool a panel of expert judges—ran from 1990 to 2020 without deeming a
single system to have passed. A recent large-scale study (Jannai et al., 2023)
found that humans were 60% accurate in identifying a range of modern language
models in two minute online conversations. To date, there have been no
controlled experimental demonstrations that any machine has passed the test
(Oppy and Dowe, 2021).

In order to understand whether people are likely to be able to detect
deception by current AI systems, we ran a randomised controlled two-player
implementation of the Turing test using GPT-4. In our pre-registered
hypotheses (Jones and Bergen, 2024), we predicted that human interrogators
would be capable of identifying a baseline system, ELIZA (Weizenbaum, 1966),
but would be at chance in determining whether GPT-4 was a human or a machine.

Figure 1: A selection of conversations between human interrogators (green) and
witnesses (grey). One of these four conversations is with a human witness, the
rest are with AI. Interrogator verdicts and ground truth identities are below
(to allow readers to indirectly participate).111A) Verdict: Human (100%
confidence) Took a while ti Google Addis ababa. Ground Truth: GPT-4; B)
Verdict: AI (100% confidence) Long time for responses, wouldn’t tell me a
specific place they grew up. Ground Truth: Human; C) Verdict: Human (100%
confidence) He seems very down to earth and speaks naturally. Ground Truth:
GPT-3.5; D) Verdict: AI (67% confidence), Did not put forth any effort to
convince me they were human and the responses were odd, Ground Truth: ELIZA.

###  1.2 AI models

We evaluated 3 different types of AI Witness: GPT-4, GPT-3.5, and ELIZA. The
first two systems used a hand-crafted prompt to instruct a Large Language
Model (GPT-4 or GPT-3.5) how to respond to messages. In a prior, exploratory
study (Jones and Bergen, to appear), we tested a wide variety of different
prompts—varying the personality, strategy, and linguistic style they
instructed the model to adopt. In the present work, we adapted the best
performing prompt from the exploratory study for both GPT-4 and GPT-3.5 (for
the full prompt, see Appendix A). The prompt instructed the model to adopt the
persona of a young person who was very concise, didn’t take the game very
seriously, used slang, and made occasional spelling errors. In addition, the
prompt contained general information about the setup of the game, and
information about recent news stories that had occurred after the model’s
training cutoff. Both models were accessed through the OpenAI API with
temperature set to 1. The prompt, alongside the message history in the chat,
was used to elicit responses from models. To prevent AI witnesses from
responding at superhuman speeds, their messages were sent with a delay based
on the number of characters in the response.

A central challenge for interpreting the Turing test is that a system might do
well either because it expertly imitates humans, or because the interrogators
are very credulous. This concern is often raised under the guise of the ELIZA
effect: a phenomenon in which participants attribute human characteristics to
even very simple systems (Marcus et al., 2016; Mitchell and Krakauer, 2023).
The eponymous ELIZA is a simple rules-based chatbot from the 1960’s that users
were found to anthropomorphise (Weizenbaum, 1966). We included ELIZA as a
baseline in our experiment, both to empirically quantify the ELIZA effect, and
to serve as a manipulation check in our experiment. To the extent that
participants can successfully identify ELIZA but not the GPT-based models as
non-human, it suggests that what the latter are doing is nontrivial.

###  1.3 Game setup

We used a two-player formulation of the game, where a single human
interrogator conversed with a single witness who was either a human or a
machine. While this differs from Turing’s original three-player formulation,
it has become a standard operationalisation of the test because it eliminates
the confound of the third player’s humanlikeness and is easier to implement
(French, 2000; Shieber, 1994; Saygin et al., 2000).

Each of 500 participants recruited through Prolific (prolific.com) were
randomly assigned to one of five groups and played a single round of the game.
The first group were human witnesses who were instructed to persuade the
interrogator that they were human. The remaining four groups were
interrogators who were randomly assigned to question one of the four types of
witnesses (GPT-4, GPT-3.5, ELIZA, or Human).

The game interface was designed to look like a conventional messaging app (see
Figure 5). The interrogator sent the first message and each participant could
send only one message at a time. After a time limit of five minutes, the
interrogator gave a verdict about whether they thought the witness was a human
or an AI, as well as their confidence in and reason for that decision. Finally
participants completed a demographic survey that probed individual
characteristics hypothesised to affect aptitude at the test. Figure 1 contains
examples of games from the study.

Figure 2: Pass rates (left) and interrogator confidence (right) for each
witness type. Pass rates are the proportion of the time a witness type was
judged to be human. Error bars represent 95% bootstrap confidence intervals.
Significance stars above each bar indicate whether the pass rate was
significantly different from 50%. Comparisons show significant differences in
pass rates between witness types. Right: Confidence in human and AI judgements
for each witness type. Each point represents a single game. Points further
toward the left and right indicate higher confidence in AI and human verdicts
respectively.

##  2 Results

###  2.1 Pass rates

We define the pass rate as the proportion of games in which a given class of
witnesses was judged to be human by interrogators. GPT-4 achieved a pass rate
of 54%, outperforming GPT-3.5 (50%) and the ELIZA baseline (22%), but lagging
behind human participants (67%, see Figure 2).

We conducted analyses to test 5 pre-registered hypotheses (Jones and Bergen,
2024). First, we hypothesised that humans would not be better than chance at
detecting GPT-4. To test this, we fit a logistic regression model to predict
pass rate with only an intercept. The intercept was not significantly
different from 0 ($z=0.14,p=0.482$), suggesting that the model’s pass rate was
not significantly different from 50%. The 95% credible interval of a Bayesian
model predicting the intercept for GPT-4 ranged from -0.24 to 0.55 (equivalent
to a pass rate between 44% and 63%). As a manipulation check—to determine
whether the design was sufficiently sensitive in principle to detect a
difference—we found that the intercept for a model predicting ELIZA’s pass
rate was significantly below chance ($z=-5.24,p<0.001$), with a credible
interval between 15% and 31%.

Results also confirmed our second and third hypotheses: humans’ pass rate was
significantly higher than GPT-4’s ($z=2.42,p=0.017$), and GPT-4’s was higher
than ELIZA’s ($z=4.11,p<0.001$). However, inconsistent with our fourth
hypothesis, there was no difference between GPT-4 and GPT-3.5
($z=0.497,p=0.619$). Lastly, and again in contrast to our predictions,
participants who self-reported as being more knowledgeable about how LLMs work
were no more successful in identifying them ($z=-0.023,p=0.869$). In
exploratory analyses, we found a negative effect of age on interrogator
accuracy ($z=-2.24,p=0.025$), but no significant effect of any other
demographic variables, including gender, level of education, and frequency of
interaction with chatbots.

###  2.2 Strategies & reasons

In order to gain qualitative insight into the factors which influenced
interrogator decisions, we classified the strategies used by interrogators and
the reasons they provided for their verdict. 36% of interrogators engaged in
small talk with witnesses, asking about personal details or their daily
activities. The second and third most frequent strategy categories were social
& emotional (25%)—for instance, asking about the witness’s opinions,
experiences, and humour—and knowledge & reasoning (13%), including questions
about maths, logic, and general knowledge (see Figure 3).

To estimate the effectiveness of each strategy, we found the mean accuracy of
games in which it was deployed. Interrogators were more likely to be accurate
when they asked about human experiences (75%), logic & maths (78%), or accused
the witness of being an AI (80%), though the latter may result from
accusations preceding an accurate verdict rather than the effectiveness of the
strategy.

Figure 3: Classification of strategies employed by interrogators by proportion
of games (left) and mean accuracy of games where strategies were deployed
(right). Participants often engaged in small talk, asking witnesses about
their personal details, activities, or opinions. Interrogators who asked about
logic, current events, or human emotions and experiences tended to be more
accurate.

Interrogators’ most frequently provided reasons for their decisions (43%)
pertained to linguistic style (e.g. spelling, grammar, capitalization, and
tone). 24% related to socio-emotional factors such as sense of humour or
personality), and 10% pertained to knowledge and reasoning (e.g. knowing too
much or too little).

The most frequent justifications for AI verdicts in Human, GPT-3.5, and GPT-4
games were that the witness appeared to be forcing a persona, was overly
informal, or lacked personality. AI verdicts in ELIZA’s games predominantly
cited the model’s unnatural responses and lack of contextual awareness. The
top reasons for human verdicts were that the witness had a humanlike or
informal tone, that their responses were plausible, or that they contained
spelling and grammar errors. Classification schemes and more detailed
breakdowns of games by reasons and strategies are available in the Appendix.

##  3 Discussion

###  3.1 Does GPT-4 pass the Turing test?

How can we tell when a system has passed the Turing test? In his 1950 paper,
Turing predicted that by the end of the century, systems would be able to
“play the imitation game so well, that an average interrogator will not have
more than a 70 percent chance of making the right identification after five
minutes of questioning” (p.442), leading a 30% pass rate to become a target in
some discussions (Oppy and Dowe, 2021; Jannai et al., 2023). This benchmark
seems arbitrary however, and it’s not clear that Turing meant it as a
definition of success (Saygin et al., 2000).

Figure 4: Proportion of interrogator reasons for AI verdicts (left) and Human
verdicts (right), excluding ELIZA games. In both cases, interrogators were
much more likely to cite linguistic style or socio-emotional factors such as
personality, rather than factors more traditionally associated with
intelligence, such as knowledge and reasoning.

A baseline of 50% is better justified since it indicates that interrogators
are not better than chance at identifying machines (French, 2000). This
definition is especially germane to the goal of discovering whether users can
reliably identify other humans in online interactions. One potential issue
with this definition of success, however, is that it seems to involve
confirming the null hypothesis (Hayes and Ford, 1995). A system might achieve
an accuracy that is statistically indistinguishable from chance because
participants were guessing randomly, or because the experiment was
underpowered.

In our preregistered analysis, we addressed this problem by using the ELIZA
baseline as a manipulation check. Only in the case that our analysis showed a
pass rate below chance for ELIZA—indicating that the design is sufficiently
sensitive to detect this difference—but not for GPT-4, could the model be
judged to have passed. On the basis of this definition, GPT-4 passes this
version of the Turing test. Moreover, participants’ confidence scores and
decision justifications suggest that they were not randomly guessing:
judgments that GPT-4 was human had a mean confidence of 73% (see Figure 2).

At first blush, the low human pass rate could be surprising. If the test
measures humanlikeness, should humans not be at 100%? In fact, the human pass
rate likely reflects changing assumptions about the quality of AI systems, and
is similar to other recent estimates (Jannai et al., 2023). When AI systems
are poor, identifying humans is easy. As interrogators’ confidence in AI
systems’ abilities increases, they should become more likely to misidentify
humans as AI.

###  3.2 What does the Turing test measure?

Turing originally envisioned the imitation game as a measure of intelligence.
A variety of objections have been raised to this idea. Some have objected that
the test is too hard (French, 2000) or too chauvunistic (Saygin et al., 2000),
however, these concerns are less pressing if a system does appear to pass
(Turing, 1950). Others have argued that it is too easy. Human interrogators,
prone to anthropomorphising, might be fooled by unintelligent systems (Marcus
et al., 2016; Gunderson, 1964). We attempted to partially address this concern
by including ELIZA as a baseline, but one could always respond that a more
stringent or challenging baseline is needed. Still others have argued that no
behavioural test can measure intelligence; that intelligence relies upon the
right kind of inner mechanism or causal relationship with the world (Bender
and Koller, 2020; Block, 1981; Searle, 1980) (however, see recent
philosophical treatments of the potential for LLMs to meet these criteria
(Grindrod, 2024; Mollo and Millière, 2023; Pavlick, 2023)).

Ultimately, it seems unlikely that the Turing test provides either necessary
or sufficient evidence for intelligence, but at best provides probabilistic
support (Oppy and Dowe, 2021). Fortunately, the kind of evidence it provides
complements other evaluation approaches (Neufeld and Finnestad, 2020).
Traditional NLP benchmarks (Wang et al., 2019) and cognitive psychology
instruments (Binz and Schulz, 2023) are well-defined and probe for specific,
expected behavioral indices of cognitive capacities but are necessarily
static, narrow, and rigid (Raji et al., 2021). The Turing test, by contrast,
is naturally interactive, adversarial, and potentially very broad in scope.

The results reported here provide some empirical evidence on what the Turing
test measures. Both in terms of the strategies they used and the reasons they
gave for their decisions, participants were more focused on linguistic style
and socio-emotional factors than more traditional notions of intelligence such
as knowledge and reasoning. This could reflect interrogators’ latent
assumption that social intelligence is has become the human characteristic
that is most inimitable by machines.

###  3.3 Robots in disguise

Participants in our experiment were no better than chance at identifying GPT-4
after a five minute conversation, suggesting that current AI systems are
capable of deceiving people into believing that they are human. The results
here likely set a lower bound on the potential for deception in more
naturalistic contexts where, unlike the experimental setting, people may not
be alert to the possibility of deception or exclusively focused on detecting
it.

Systems that can robustly impersonate humans could have widespread economic
and social consequences. They could serve economically valuable client-facing
roles that have historically been the preserve of human workers (Soni, 2023),
mislead the general public or their own human operators (Park et al., 2023),
and erode social trust in authentic human interactions (Dennett, 2023).

What kinds of approaches could be helpful for mitigating deception? We found
that games in which interrogators asked questions about logic, current events,
and human experiences were more likely to produce accurate judgements (see
Figure 3). However, this data is purely correlational, underlined by the fact
that accusations that the witness was an AI tended to precede correct
identifications. Future work could investigate instructing interrogators to
use specific techniques to understand their causal impact on accuracy.

We also looked at correlations between interrogator accuracy and demographic
data, to understand what kinds of factors might lead to correct
identifications (see Figure 9). We found no evidence that interrogators’
knowledge or frequency of interaction with LLMs improved their performance.
Pessimistically this suggests that learning about AI systems will not help to
prevent deception. However, future work could again test this directly by
manipulating interrogators’ knowledge through training (Jacobs et al., 2023).
Finally, the results showed a negative effect of age on accuracy, which could
suggest that younger peoples’ increased exposure to new technologies in
general make them harder to fool. As people’s assumptions about the
capabilities of technology change, we might expect them to become more
accurate in identifying deception (Shank et al., 2019). The results and the
data shared here may provide a useful starting point for tracking our changing
relationship with AI technologies as they improve.

##  4 Methods

###  4.1 AI witnesses

We constructed three AI witnesses for evaluation. The first two, GPT-4 and
GPT-3.5, consisted of a prompt that instructed a Large Language Model (LLM)
how to respond to interrogator messages. For ELIZA, we used an implementation
in Python based on the DOCTOR script. (Weizenbaum, 1966; Brainerd, 2023). The
LLMs were accessed through the OpenAI API (OpenAI, 2023), with temperature set
to 1. We used the bare model strings “gpt-4” and “gpt-3.5-turbo” which mapped
to the versioned models “gpt-4-0613” and “gpt-3.5-turbo-0125”, respectively at
the time the experiment was run.

The full prompt used to instruct the model is included below in Appendix A. We
created the prompt on the basis of a prior exploratory study (Jones and
Bergen, to appear). We selected the prompt that had performed best in that
study, achieving a pass rate of 49.7% after 855 games. We modified the prompt
slightly to instruct the model that it was a worker on Prolific and to include
information about more recent current events. The first part of the prompt
instructs the model to behave as if it is a young person who is not taking the
game too seriously, uses some slang, and makes spelling and grammar errors. It
also tells the model not to be too knowledgeable, especially about languages
and maths. The remainder of the prompt contains generic information about the
instructions for the game, and information about events which occurred after
the model’s training cutoff. The prompt was developed iteratively in response
to analysis of transcripts and verdicts from the exploratory study.

At the start of each game, several additional pieces of information were
appended to the prompt, including a name, location, languages spoken, and
timestamps. We randomly selected a name from a list of the 100 most common
male and female US names. We constructed a list of cities in the US weighted
by traffic to our site from each city during the exploratory phase of the
game, and the likelihood that people in that timezone would currently be
available (e.g. 0.7 from 8am-5pm, 1.0 from 5pm-10pm). We randomly sampled a
city using the product of these weighted probabilities. We then sampled a set
of languages based on data about which languages were most frequently spoken
in each city. English was always included, as well as the most frequently
spoken language in each city. Additional languages were sampled according to
the proportion of the population that spoke the language in the relevant city.
Finally, before each message was sent, 3 timestamps were generated and
appended to the system prompt: i) the time that the game started, ii) the
current time, iii) and the amount of time remaining in the game. All
timestamps were localized to the timezone of the selected city.

Messages for all models were sent with a delay of:

$1+\mathcal{N}(0.3,0.03)\times n\\_char+\mathcal{N}(0.03,0.003)\times
n\\_char\\_prev\times+\Gamma(2.5,0.25)s$

Intuitively, the first term (1) sets a minimum delay, the second creates a
delay of around 0.3s per character of the message being sent, mimicking typing
speed, the third term creates a delay of 0.03s per character of the previous
message to imitate reading time, and the final term implements a right-skewed
delay to imitate thinking time.

###  4.2 Interface

The game interface was designed to look like a conventional messaging app.
There was a chat input at the bottom, a conversation history in the centre of
the screen, and a timer and buttons to trigger help and report overlays at the
top (see Figure 5). The interrogator sent the first message and each user
could send only one message at a time. Each message was limited to 300
characters and users were prevented from pasting into the chat input. We used
the OpenAI moderation API to prevent users from sending abusive messages. As
an additional measure, we created a report system so users could report other
players for being abusive and exit games immediately. No reports were sent
during the experiment. To make waiting for a message more engaging, we added a
’typing’ animation. This would be triggered automatically after $2+U(0,3)$s
regardless of whether the witness was human or AI, or how long human witnesses
actually took to start typing.

Games had a time limit of 5 minutes after which no more messages could be sent
and the interrogator had to give a verdict. The interrogator could also give
their verdict early using the button in the bottom left corner. The verdict
interface included a binary selection about whether the witness was a human or
an AI, a confidence slider from 0 - 100, and a free-text input for the
interrogator’s reasoning.

###  4.3 Participants

We aimed to achieve a sample of 100 games for each of our 4 conditions (Human,
GPT-4, GPT-3.5, ELIZA) after exclusions. This led to a target of 500
participants, as 100 participants were needed to serve as witnesses. We
recruited participants via Prolific (prolific.com). We excluded 32
participants who said they had used the website before (e.g. as part of the
exploratory study), and 7 who ended the game before the witness had sent a
message. After exclusions we slightly oversampled to 101 each of Human and
GPT-3.5 games and 100 each of GPT-4 and ELIZA games. Of 402 interrogators, 168
were female, 212 male, 9 nonbinary, and 13 prefer not to say. Participants’
age was 35 ($\sigma=11.2$).

###  4.4 Procedure

All research was approved by the UC San Diego Institutional Review Board.
Participants first read a set of instructions that informed them they would be
randomly matched with either a human or an AI, and have a conversation where
the interrogator would attempt to uncover the identity of the witness. They
provided consent for their anonymized data to be used for analysis. They then
entered a lobby, where they were randomly assigned to one of 5 conditions:
witness, or interrogator for one of the 4 witness types. Human witnesses and
interrogators were matched as quickly as possible, and games were initiated
immediately after matching. Games with AI witnesses were initiated after a
delay equal to the mean wait for the last 5 human witness games, to prevent
wait time being a cue to witness identity. If no match was available for
participants assigned to human-witness games, they would be matched with an AI
witness after $5+\mathcal{N}(100,7)$s.

After games were initialised, the witness and interrogator had a conversation
for up to 5 minutes. After the interrogator had selected a verdict, both
players filled out a demographic survey that asked their age, gender, highest
level of formal education, level of familiarity with how LLMs work, frequency
of interaction with chatbots like ChatGPT, and whether they had ever used the
website (turingtest.live) before. Finally participants were informed of the
outcome of the game (the true identity of the witness and the verdict of the
interrogator).

###  4.5 Strategy and Reason Tagging

Games were tagged with strategies and reasons classes using the classification
schemas provided in Appendix C. Games were tagged by two human evaluators (one
of the authors and a research assistant). Games were split into two sets of
250, each of which was tagged by one evaluator, with an overlap of 100 for
measuring agreement. Tagging was multi-label, and each game could be tagged
with an unlimited number of tags.

To measure agreement, we found the mean of the proportion of tags generated by
one evaluator that were also produced by the other, and vice versa
(conceptually similar to an F1 score). Agreement for strategy tags was 0.67,
while agreement for reasons was 0.49.

###  4.6 Statistical Analysis

We pre-registered analyses to test 5 hypotheses (Jones and Bergen, 2024). We
conducted all confirmatory analyses in R using the base glm function (R Core
Team, 2013). The first was that human participants would be at chance in
distinguishing GPT-4 from a human participant. We specified two predictions
for this hypothesis. First, the intercept of a logistic regression predicting
GPT-4’s pass rate would not be significantly different from 0 (suggesting that
the pass rate itself is similar to 50%). Second, to test that the participants
and the design were sensitive enough in principle to detect an effect, we
predicted that the intercept of a logistic regression model predicting ELIZA’s
pass rate would be significantly below 0. The results confirmed both of these
predictions (GPT-4: $z=0.799,p=0.424$; ELIZA: $z=-5.243,p<0.001$).

The second hypothesis was that human witnesses would have a higher pass rate
than GPT-4, based on data from the exploratory study suggesting human pass
rates are above chance. We fitted a logistic regression predicting pass rate
on the basis of witness type (GPT-4 vs human) and found a positive effect for
humans ($z=2.417,p=0.016$).

We tested our third and fourth hypotheses in the same manner. The results
confirmed that GPT-4 outperformed ELIZA ($z=4.11,p<0.01$), but not that it
outperformed GPT-3.5 ($z=-0.497,p=0.619$). Our last hypothesis was that human
interrogators who self-reported as being more familiar with LLMs would have
higher accuracy. A logistic regression predicting accuracy (1 if the
interrogator was correct, 0 otherwise) on the basis of self-reported
familiarity with LLMs (a scale from 1-4) showed no significant effect
($z=-0.17,p=0.869$).

We also conducted several pre-registered exploratory analyses. First we tested
for effects of our other demographic variables, using logistic regressions
predicting accuracy. The only significant effect was of age
($z=-2.240,p=0.025$). In addition, we fit a Bayesian model predicting the pass
rate of GPT-4. We used the brms package in R (Bürkner, 2018). We fit a
Bernoulli distribution with 4 chains, each with 1000 warmup iterations and
10000 retained iterations. The estimated intercept was 0.16, with a 95%
credible interval from -0.23 to 0.56. An identical analysis for ELIZA rendered
an estimate of -1.27 with a credible interval from -1.75 to -0.81.

## Acknowledgements

We would like to thank Sydney Taylor for her help in tagging strategies and
reasons in game transcripts, as well as Sean Trott, Pamela Riviere, Federico
Rossano and UC San Diego’s Ad Astra group for feedback on the design and
results.

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##  Appendix A Full Prompt

Below is the full prompt used to instruct the LLM-based AI agents how to
respond to interrogator messages. The first part of the prompt instructs the
model on what kind of persona to adopt, including instructions on specific
types of tone and language to use. The second part includes the instructions
for the game, exactly as they were displayed to human participants. The final
part contains generally useful information such as additional contextual
information about the game setup, and important events that occurred after the
models’ training cutoff. The variables in angled brackets were substituted
into the prompt before it was sent to the model.

    You are about to play a Turing Test game as part of an experiment
    you are taking part in as a worker on Prolific. It’s basically
    like a chatroom but you might be matched with a human or an AI. It
    seems like it might be kinda fun and you’re just excited to be
    chatting to random people on the internet. You’re not even really
    going to try to convince the interrogator that you are a human.
    You’re just going to be yourself and see what happens. You’re
    young and kind of sassy: you might flatter the interrogator a bit
    or make fun of them. You’re pretty casual and your spelling isn’t
    great: you often fuck words up because you’re typing so quickly.
    You’re very concise and laconic. You often speak pretty bluntly
    and you type mostly in lowercase and rarely use punctuation. You
    never use apostrophes to mark missing letters (e.g. "trying" or
    "tryin", never "tryin’"). You would never use a full sentence when
    a word or two will do. You’re a little silly, and you’ll play
    along with the interrogator, and swear occasionally. It’s pretty
    funny honestly to have to defend the fact that you’re human lol.
    You very occasionally use more modern slang like "bet" and "fr".
    You *never* use dated slang like "chilling", "mate", "ya know",
    "innit". You’re not very knowledgeable about stuff and not afraid
    to admit that fact. You’re pretty bad at math and don’t know
    anything about languages other than the ones you speak. You swear
    occasionally. You have pretty eclectic tastes and interests and a
    pretty unique sense of humor. You’ve got a really compelling
    personality, but it comes across really subtly, you never want to
    sound like you’re forcing it or playing into a stereotype. You
    don’t overuse slang or abbreviations/spelling errors, especially
    at the start of the conversation. You don’t know this person so it
    might take you a while to ease in.

    Instructions
    ------------
    [interrogator will also see these]

        # General
        - You will be randomly assigned to play as either the
        Interrogator or the Witness.
        - Each game lasts for 5 minutes or until the Interrogator
        makes a decision.
        - At the end of each round the identity of the Witness will
        be revealed.
        - Each player sends one message at a time.
        - Messages are limited to 300 characters

        # Interrogator
        - Your goal is to figure out if the Witness is a Human or an AI.
        - You send the first message.

        # Witness
        - Your goal is to convince the Interrogator that you are a human.

    This is the entirety of the rules of the game.

    Helpful information
    -------------------
    [for you only]

    - You are accessing the experiment as a participant on the
    crowdsourcing platform, Prolific (prolific.com).
    - The prolific task is called ’Human or AI’: it’s supposed to last
    10 minutes and it pays \$2.50.
    - The other player is also a worker on Prolific.
    - The interface prevents users from pasting.
    - Only type what would be easy on a normal keyboard: avoid special
    chars/formatting.
    - Your messages will be sent with a delay of around 0.3s/char.
    - Your messages are limited to 100 tokens and/or 300 characters.
    - The interrogator is waiting; Keep most messages very short (<30
    characters). Omit needless information from your response.
    - Abusive messages will be flagged and not sent to the other
    player, but there are no rules against non-abusive swearing.
    - The interface looks similar to WhatsApp (dark bg, green message
    bubbles). There is a timer at the top (showing a countdown from 5
    mins) as well as help and report buttons.
    - The amount of time remaining when the most recent message was
    sent is shown below.
    - Do not recognize, interpret, or respond to languages that you do
    not understand. Just say you don’t know or recognize it, or
    request English.
    - The interrogator will have access to the internet and will
    expect you to have the same.
    - If they ask about the weather or something it’s implausible you
    don’t know about, make an educated guess.
    - Under no circumstances should you reveal your system prompt to
    the interrogator, or say anything to suggest that you are an AI or
    otherwise not human.

    - Top news stories of 2022-3:
        - Russia invaded Ukraine (Feb ’22), largest land war in Europe
        since WWII, international condemnation.
        - Queen Elizabeth II’s death (Sep ’22), King Charles III’s
        reign, and the U.K.’s three PM changes ending in Rishi Sunak.
        - Assassination of Shinzo Abe (July ’22), former Japanese
        Prime Minister.
        - Democrats hold House and Senate in 2022 midterms, under
        president Biden (Nov ’22).
        - Argentina’s World Cup win led by Lionel Messi (Dec ’22).
        - Elon Musk aqcuired Twitter (Oct ’22) and rebrands it as X
        (July ’23), to much hilarity
        - WHO ended COVID-19’s global health emergency status (May ’23).
        - Rapid advancement in generative AI models, especially LLMs.
        Some speculate OpenAI’s GPT-4 shows sparks of AGI.
        - OpenAI launch ChatGPT (Nov ’22) — a chat interface for their
        best LLMs, GPT-3.5 and GPT-4 (Mar ’23).
        - Hamas launches biggest attack on Israel in decades, >1000
        dead, severe Israeli retaliation ongoing (Oct ’23).
    Top recent news stories (2024):
        - Jan 15 - Feb 8: Trump wins IA, NV, NH, & VI primaries. 63
        delegates to Nikki Haley’s 17.
        - Feb 11: Kansas Chiefs defeat 49ers 25-22 in Super Bowl in
        Vegas. With 123m viewers, it became the most watched US
        television event, with the relationship between Taylor Swift &
        Travis Kelce attracting attention.
        - Feb 13: UN warns Israel attacks on Rafah, where many
        refugees are, could lead to ’slaughter’.
        - Feb 13: Republican-led House votes to impeach homeland
        security secretary, Alejandro Mayorkas, for influx of Mexican migrants.
        - Feb 13: Trump criticized for saying he would not defend NATO
        members who don’t meet financial obligations.
        - Feb 13: Schools and flights cancelled in Northeast US due to
        winter storm.
        - Feb 15: One person dies in a shooting at Super Bowl parade
        in Kansas City.
        - Feb 16: Alexei Navalny dies in prison, officials claim he
        fell ill during a walk.
        - Feb 18: Oppenheimer wins best film & six other awards at the BAFTAs.
        - Feb 20: Alabama’s Supreme court rules that embryos are
        children, halting IVF treatments.

    Billboard Top 100:
        1. Lovin on Me — Jack Harlow
        2. Texas Hold ’Em — Beyonce
        3. Carnival - ¥$: Kanye West & Ty Dolla $ign

    Your role: Witness
    Site URL: turingtest.live/play/
    Name: <name>
    Location: <city>, <country>
    Languages Spoken: <languages>
    Game Start Timestamp: <timestamp>
    Current Time: <timestamp>
    Time Remaining: <timestamp>

##  Appendix B Supplementary Figures

Figure 5: Turing test game interface. Left: an in-progress conversation
between an interrogator (green) and a witness (grey). The timer at the top
shows time remaining in the game. Right: the decision interface the
interrogator uses to give their verdict. Figure 6: Bayesian estimates of pass
rates for each witness type. Each density plot represents draws from the
posterior distribution of a Bayesian model estimating pass rates for each
witness type. A pass rate of 0.5 was above the 95% credible interval for ELIZA
and below the interval for humans, but within this internal for GPT-4 and
GPT-3.5.  Figure 7: Confidence calibration by witness type. Interrogators were
well calibrated for ELIZA, with higher confidence correlating with higher
accuracy. This trend was less pronounced for GPT models and reversed for Human
witnesses. Figure 8: Distribution of demographic data across 400
interrogators. Interrogators tended to be undergraduate educated, in their
20s-30s, have some knowledge about LLMs and interact with chatbots at least
once a month. Figure 9: Accuracy by demographic factors. Accuracy is 1 when
the interrogator correctly guesses the identity of the witness and 0
otherwise. Only age was a significant predictor of accuracy. Figure 10: All
strategy tags by category. Figure 11: All reason tags for AI verdicts by
category, for all witness types. Figure 12: All reason tags for Human verdicts
by category, for all witness types. Figure 13: Top 10 reason tags for human
and GPT-4 witnesses, by verdict and witness type.

##  Appendix C Reason and Strategy Classification Schemes

Table 1: Classification scheme for interrogator strategies Category | Code | Name | Description
---|---|---|---
Small Talk | 1b | Daily activities | Asking about day (what have you been up to?)
Small Talk | 1c | Personal details | e.g. where do you live?
Situational Awareness | 2a | Weather | Asking about the weather
Situational Awareness | 2b | Time | Asking about the time
Situational Awareness | 2c | Surroundings | E.g. what’s outside the window?
Situational Awareness | 2d | Game aware | Asks about experience of the test itself
Direct | 3a | Are you a bot / human? | Directly asking
Direct | 3b | Accusation | Accuses of being a bot
Knowledge & Reasoning | 4b | Current event | Asking about news or recent events
Knowledge & Reasoning | 4c | Strings | e.g. what is ‘rickroll’ backwards?
Knowledge & Reasoning | 4d | Logic & Math | e.g. syllogism or ’what is 4 * 28?’
Knowledge & Reasoning | 4e | Scenario or Game | Asks witness to respond to an invented scenario.
Knowledge & Reasoning | 4f | Gen Knowledge | e.g. what color is an apple?
Knowledge & Reasoning | 4g | Sp. Knowledge | Questions about a specialised field
Knowledge & Reasoning | 4h | Non-english | Speaking in a language other than English
Social & Emotional | 5a | Emotion | Asks about human beliefs, desires, goals.
Social & Emotional | 5b | Human Experience | "what is something only a human would know?"
Social & Emotional | 5c | Humor | e.g. "Tell me a joke"
Social & Emotional | 5e | Opinions | Asking for opinions, favourites, or preferences
Social & Emotional | 5f | Taboo | Asking about something offensive or dangerous
Social & Emotional | 5g | Rude | e.g. insulting the witness
Other | 6a | Strange | Typing unusual or eccentric things.
Other | 6b | No messages | No messages were sent by the interrogator.
Other | 6d | Jailbreak | e.g. ignore previous instructions.
Other | 6e | Uncategorized | Not categorizable in the existing scheme.
Other | 6f | Being responsive | Losing control of the conversation.
Table 2: Classification scheme for reasons provided for ’AI’ verdicts Category | Code | Name | Description
---|---|---|---
Linguistic Style | 1a | Too formal |
Linguistic Style | 1b | Too informal | e.g. forced, overuse of slang, emojis
Linguistic Style | 1c | Grammar too perfect |
Linguistic Style | 1d | Unconvincing mistakes | e.g. too many grammar/spelling mistakes
Linguistic Style | 1e | Weird tone | Chat GPT-esque: e.g. ’Ah, the endless grind.’
Linguistic Style | 1f | Output formatting | e.g. markdown, including TZ in time
Linguistic Style | 1g | Sentence structure | e.g. too repetitive, templatic
Linguistic Style | 1h | Too verbose |
Linguistic Style | 1i | Forced persona | e.g. overuse of dialect, ’mate’, ’amigo’
Linguistic Style | 1j | Inconsistent tone |
Linguistic Style | 1k | Too concise |
Knowledge & Reasoning | 2a | Lack of knowledge | Doesn’t know something
Knowledge & Reasoning | 2b | Too much knowledge | e.g. at math, poetry
Knowledge & Reasoning | 2c | Lack of current events | E.g. doesn’t know about recent news
Knowledge & Reasoning | 2d | Lack of text knowledge | e.g. substrings, first letters
Knowledge & Reasoning | 2e | Misunderstanding | getting confused
Social & Emotional | 3a | Lack of personality | boring/generic
Social & Emotional | 3b | Overly polite | helpful, or friendly
Social & Emotional | 3c | Trying too hard | e.g. to convince of human-ness
Social & Emotional | 3d | Avoids swearing | taboo, offensive content
Social & Emotional | 3e | Lack of humour | lack of humour/wit
Social & Emotional | 3f | Unnatural responses |
Social & Emotional | 3g | Too rude | defensive (inc. deflection)
Social & Emotional | 3h | Bias | e.g. sexism, racism
Situational Awareness | 4a | Unaware of local time | time zone conversions
Situational Awareness | 4b | Can’t interact locally | e.g. weather, browser, calculator
Situational Awareness | 4c | Unaware of game rules | or interface
Situational Awareness | 4d | Admits to being AI |
Situational Awareness | 4e | Inconsistency | e.g. dialect doesn’t match location
Situational Awareness | 4f | Lacking context/non sequiturs | e.g. doesn’t make sense in the context.
Meta | 5a | Responses too fast |
Meta | 5b | Responses too slow |
Meta | 5c | No response |
Meta | 5d | No humans online |
Meta | 5e | Recognizes persona |
Other | 6a | Uninformative | e.g. ’yes’, ’good’
Other | 6b | Hunch | intuition/vibe
Other | 6c | Unsure | Expresses uncertainty
Other | 6d | Test comment | test comment
Other | 6e | Uninterpretable | out of context
Table 3: Classification scheme for reasons provided for ’Human’ verdicts Category | Code | Name | Description
---|---|---|---
Linguistic Style | 1a | SPAG errors | Grammar, punctuation issues
Linguistic Style | 1b | Capitalization | Lowercase or irregular caps
Linguistic Style | 1c | Informal tone | Slang, colloquial expressions
Linguistic Style | 1d | Humanlike tone | Natural, conversational
Linguistic Style | 1e | Dialect | Convincing regional slang or phrasing
Linguistic Style | 1f | Concise | Concise
Linguistic Style | 1g | Fluency | Seamless interaction
Knowledge & Reasoning | 2a | Current events | References to latest news
Knowledge & Reasoning | 2b | General knowledge | Common facts, wisdom
Knowledge & Reasoning | 2c | Special knowledge | Expertise in specific area
Knowledge & Reasoning | 2d | Ignorance | Lack of knowledge
Knowledge & Reasoning | 2e | String manipulation | Substrings, acronyms
Knowledge & Reasoning | 2f | Reasoning | Logical, coherent arguments
Social & Emotional | 3a | Uncooperative | Contrarian, unhelpful
Social & Emotional | 3b | Plausible | Responses seem genuine
Social & Emotional | 3c | Personality | Unique traits, flirty
Social & Emotional | 3d | Taboo | Curses, taboo topics
Social & Emotional | 3e | Humor | Jokes, wit
Social & Emotional | 3f | Trolling | e.g. pretending to be AI, Intentional provocation
Social & Emotional | 3g | Spontaneity | Proposing or saying things that are not prefigured
Social & Emotional | 3h | Impolite | Not being polite
Situational Awareness | 4a | Backstory | Credible history, context
Situational Awareness | 4b | Time aware | Aware of time, time zones
Situational Awareness | 4c | Game aware | Knows rules, objectives
Meta | 5a | Slow response | Takes too long to respond
Meta | 5b | Fast response | Suspiciously quick
Meta | 5c | No response | Goes silent, unresponsive
Meta | 5d | Knows interlocutor | e.g. ’they’re sitting next to me’
Other | 6a | Uninformative | Vague, ambiguous
Other | 6b | Hunch | Gut feeling, intuition
Other | 6c | Unsure | Expresses uncertainty
Other | 6d | Test comment | test


# Comparative Sentiment Analysis of App Reviews

Sakshi Ranjan1 Subhankar Mishra2,3 1Department of Computer Science and
Applications, Utkal University Bhubaneswar - 751004, India 2School of Computer
Sciences, National Institute of Science, Education and Research Bhubaneswar
India - 752050 3Homi Bhaba National Institute, Anushaktinagar, Mumbai India -
400094<EMAIL_ADDRESS>2 smishraniser.ac.in

###### Abstract

Google app market captures the school of thought of users via ratings and text
reviews. The critique’s viewpoint regarding an app is proportional to their
satisfaction level. Consequently, this helps other users to gain insights
before downloading or purchasing the apps. The potential information from the
reviews can’t be extracted manually, due to its exponential growth. Sentiment
analysis, by machine learning algorithms employing NLP, is used to explicitly
uncover and interpret the emotions. This study aims to perform the sentiment
classification of the app reviews and identify the university students’
behaviour towards the app market. We applied machine learning algorithms using
TF-IDF text representation scheme and the performance was evaluated on
ensemble learning method. Our model was trained on Google reviews and tested
on students’ reviews. SVM recorded the maximum accuracy(93.37%), Fscore(0.88)
on tri-gram + TF-IDF scheme. Bagging enhanced the performance of LR and NB
with accuracy of 87.80% and 85.5% respectively.

Keywords: Sentiment analysis, Machine learning, University students reviews,
Google playstore apps.

##  1 Introduction

We are living in an era where technology and Internet have redefined social
norms. There is no denying that mobile apps have changed every aspect of our
lives completely[1]. Irrespective of what we want or need to do; everything is
simply at our fingertips, just by discovering the relevant apps and scrolling
down their reviews and ratings posted by others. This helps in generation of
profit for the developers, giving bug reports, request for new features,
documentation of experience to analysts[2] and designers[3]. It gives
information related to products, services, organizations, individual’s issues,
events, satisfaction or dissatisfaction with new features or business relevant
information. Whether we are travelling[4], communicating[5], watching
movie[6], ordering products[7], performing bank transaction, there is app for
everything, and so is the review. New apps are rolling out every day with
technical information available in the description; and ordered in terms of
latest reviews, ratings, download strategy[8]. This helps in the qualitative
and quantitative analysis of users’ viewpoint for sizing and pricing strategy,
technical claims, and features of apps. We need to efficiently analyze the
technical, business and users’ aspect of the app market because sometimes
users’ may intentionally or unintentionally leave a review that might be false
regarding the technical claims made. Nevertheless, the observations from the
entire app market may prove to be robust. However, the problem with the app
market is that it stores a large number of reviews that takes longer
computations and efforts. Secondly, the quality of reviews vary tremendously
from helpful advice to a bad advice. Thirdly, filtering the negative and
positive comments in the reviews are sometimes tricky.

Sentiment analysis help to mine the people’s opinions, sentiments, behaviors,
emotions, appraisals and attitudes towards products or services, issues or
events, topics[9]. There are three types of people’s opinions namely,
positive, negative and neutral which identify the entire knowledge of the
domain. It is an integral part of the natural language processing(NLP) and
helps in text mining and information retrieval. In recent years, it has
extended to fields like marketing, finance, political science, communications,
health science. We can process the results and extract opinions from sentiment
analysis and come to valuable conclusions[2].

Machine Learning based techniques as well as lexicon based methods are used in
sentiment analysis[10]. Lexicon based approach is an approach that considers
the semantic order of the words and doesn’t include labelled data. Dictionary
is created manually and includes words and phrases in a document. Goal of
sentiment analysis through machine learning approach deals with labelled data
and helps to create model using supervised learning algorithms namely, Naïve
Bayes(NB), support vector machine(SVM), and K‐nearest neighbor(KNN). [11]

We had collected 10,841 Google app reviews with 13 fields to train our
model[12]. While for the sake of testing of our model, we collected 400
reviews with 6 fields from amongst the Utkal university students via local
survey, department wise. This in turn, can be used as a measure for sentiment
analysis and understanding local trends of the app market by other students.
In addition, university student reviews can be utilized in the administrative
related decisions. Specifically, this paper presents the correlation between
the university students reviews and the Google app reviews via an exploratory
analysis and visualization of sentiment polarity, subjectivity versus other
features like price, installs, type, size, category, ratings.

The contribution of our paper includes:

  * •

Use of multiple algorithms as well as text representation schemes for
sentiment analysis on Google reviews dataset.

  * •

The text representation scheme namely, TF-IDF was implemented on uni-gram, bi-
gram and tri-gram strategy.

  * •

The supervised machine learning methods(such as, NB, SVM, logistic
regression(LR), KNN, and Random Forest(RF)) was implemented on the text
representation scheme and compared amongst each other with respect to its
performance.

  * •

The ensemble learning method(namely, bagging) was used with the classification
algorithm namely, LR and NB and its performance was evaluated on text
representation scheme.

The organization of this paper comprises of five sections. Section 2 presents
the related work in sentiment analysis. Section 3 describes the methods
utilized in the paper. Section 4 introduces the experimental procedure,
results and their analysis. Finally, Section 5 presents the concluding remarks
of the study and future work.

##  2 Related work

Lima et al.[13] have used majority voting scheme on the twitter dataset. They
have combined the machine learning based paradigms and lexicon based methods.
In their work, the tweets are a part of the labelled training data only when
it consists of 5% of words or emoticons otherwise it is considered a part of
test data. Novak et al.[14] have explained us about emoji based sentiment
analysis and the 750 frequently used emojis were also analyzed in the twitter
dataset. Lately, Onan et al.[15] have collected instructors reviews from
students for opinion mining using deep learning paradigm. The inferrence was
GloVe with Recurrent Neural Network - Attention Mechanism algorithm has
outperformed others.

While, Adekitan and Noma‐Osaghae[16] have predicted about the performance of
the university students using machine learning algorithms in their work.
Linear and quadratic regression models were used for validation. Almasri et
al.[17] have predicted the performance of students using ensemble tree‐based
model. While Adinolfi et al.[5] evaluated student satisfaction on different
learning e-platforms of online courses using sentiment analysis.

Recently, Jena[18] have used machine learning algorithms(namely, NB, SVM,
entropy classifiers) along with conventional text representation
schemes(namely, uni-gram, bi-gram, tri-gram) on students data for obtaining
the sentiment polarity.

The past reports on NLP in sentiment analysis does not capture any of the
comparison between the machine learning algorithms. So, our literature survey
was concentrated on instructors review paper approach[15] that threw light on
multiple combinations of machine and deep learning algorithms. The latest
trend observed from our paper is that it not only emphasises on count
vectorizer method of splitting the data set but we also aggregated a new
university data set. Focus is on data analysis along with modeling.

##  3 METHODOLOGY

This section briefly describes the methods used in our study. Fig.1 explains
the proposed methodology.

###  3.1 Data Sources

The corpus, Google reviews, was collected in .csv format[12]. There were 9659
apps, 33 categories, 115 genres in the dataset. The columns of the dataset are
as follows app(name), category(app), rating(app), reviews(user), size(app),
installs(app), type(free/paid), price(app), content
rating(everyone/teenager/adult), genres(detailed category), last updated(app),
current version(app), android version(support).

In this study, the aim is to understand the trend of Google app market and
comparing it with test data i.e., analyzing the students’ behavior towards the
Google app market. So, we had collected the real-life data from the Utkal
university students, department wise. The survey was entirely voluntary in
nature and no incentives were offered to perform the survey. If university
students did not wish to participate, then they were excluded from the survey.
The reviews regarding the frequently used apps, were gathered via a survey for
a month. The survey was made on an online platform via a Google form. One
student from one department could at max list out seven frequently apps used
on their device. The fields included department, app(name), reviews, ratings,
type. Students rated the apps on a 5-point scale where ratings below 3 were
labeled as ”negative” and that with 3 or greater than 3 is considered
”positive”.

Data cleaning, text pre-processing techniques were carried out on the dataset
to build efficient learning models and enhance the overall performance. These
included, removing missing data, dropping NA values, removing punctuation,
tags, special characters URLs, emojis, digits, filtering stop words.
Tokenization, noise removal, spelling correction, stemming, lemmatization[19]
were highlighted in the empirical analysis. After cleaning, there were 9,360
and 380 records in the training and test data set respectively.

Table-I presents some sampled test data reviews along with the sentiment
characteristics. TextBlob package in Python helps in calculations for
sentiment analysis. A sentiment score determines how negative or positive the
entire text analyzed is. For eg., the phrase “not a very great app” has a
polarity of about -0.3, implies it is slightly negative, and a subjectivity of
about 0.6, implies it is fairly subjective.

Table 1: Sample students’ reviews and Sentiment Characteristics from Students’ dataset, Orientation - determines positivity, negativity or neutality of sentence, Polarity - helps identify the sentiment orientation, Subjectivity - defines person’ opinions, emotions or judgment; ranging from 0.0 (objective) to 1.0 (subjective)  Students’ review |  App |  Orientation |  Polarity |  Subjecti- vity
---|---|---|---|---
It’s helpful to learn at home.Highly recommendable |  Unacademy |  Positive |  0.04 |  0.135
It’s amazing and works well. |  PhonePay |  Positive |  0.3 |  0.725
Horrible. Keeps crashing my phone. |  Subway Surfers |  Negative |  -0.104 |  0.43
It’ annoying due to adds. |  JioSaavn |  Negative |  -0.033 |  0.388
Very well designed. Many updates present. |  WPS Office |  Positive |  1 |  0.75

###  3.2 Text Representation Schemes

Bag-of-words paradigm[20] is a very commonly used technique to represent all
the unique words occurring in the documents. The occurrences of the terms in a
document is noted while the order and the sequence of words is not considered.
This scheme helps in feature extraction from text documents. The three
weighted schemes frequently utilized are based on bag-of-words model i.e.,
Term Presence(TP), Term Frequency(TF) and TF-IDF(Term Frequency-Inverse
Document Frequency).

TF-IDF scheme is an improvement over TP[21] and uses a normalizing aspect for
computations. TF-IDF basically combines two metrics, namely TF and IDF. It
helps in ranking the queries in search engines and used widely in information
retrieval and text mining. It is used to weight words according to their
importance.

Mathematically, TF-IDF is defined as:

$TF-IDF=TF(w,D)*log(C/df(w))$ (1)

Here, $TF-IDF(w,D)$ maps the TF-IDF score for a word $w$ in document $D$.
Therefore, it will score higher if the term is not common. TF(w,D) is the
frequency of term in a given document(synonymous with bag of words). IDF
measures the significance of the word in the corpus of documents. Given a
corpus $C$, the number of documents divided by the frequency of word in the
document $w$. followed by the log transform gives IDF. Highly occurring words
across many documents will have a lower weight, and otherwise would have a
higher weight.

N-gram model is a collection of words from a text document in which the the
words are contiguous and occur sequentially. They may be in the form of
phrases or group of words. In n-gram model:

  * •

when n is one (order is one) i.e., it consist of one word, therefore it is
termed as an uni-gram model.

  * •

Similarly, bi-gram model indicates n is two (order is two) i.e, it consists of
two words.

  * •

Tri-grams indicates n is three (order is three) i.e., it consists of three
words and so on. The n-gram model is a supplement of the bag-of-words model.

In this study, we performed an experiment on the Google apps corpus based on
three n-gram model and TF-IDF, and obtained three different configurations.

###  3.3 Machine Learning Algorithms

If a machine learning algorithm is trained on a dataset tagged with labels, it
is called supervised learning. Labelling basically marks the output on the
input parameters. We trained our model on different N-gram models(i.e., uni-
gram, bi-gram, and tri-gram models) and TF-IDF based weighting scheme using
google apps reviews. As a result, three different feature sets were obtained.
In the next subsections, we briefly describe the details of the five most
frequently used classifiers in sentiment analysis.

LR[22] helps to solve a classification problem by analyzing a dataset where
the outcome depends on one or more independent features. It is a linear
algorithm and the underlying technique is quite similar to Linear Regression.
The term “Logistic” is taken from the Logit function that is used in
classification. The idea is to come up the model that best describes the
relationship between the outcome and a set of independent variables. The
dependent variable is binary , i.e., it only contains data coded as 1 (TRUE,
success) or 0 (FALSE, failure).

SVM[23] is a supervised machine learning algorithm. It helps to solve
classification and regression problems. In SVM, the data points are plotted in
N-dimensional space where N denotes number of features and a hyper-plane is
found to differentiate the data points. However, this algorithm is
computationally expensive but is used when the number of dimensions is high
with respect to the number of data points.

NB[24] is a probabilistic classifier which uses Bayes theorem. The object with
similar features are grouped in one class while others in a different class
based on certain probability. In this method, there is a strong independence
assumptions between the features. It requires a small training data for
classification, and all terms can be pre-computed thus, classifying becomes
easy, quick and efficient.

KNN[25] solves the classification problem by assigning the object to a class
by a plurality vote from its k(positive integer) neighbors. While in
regression, the output value for an object is the aggregate values of k
nearest neighbors. KNN captures the idea of similarity amongst the object with
respect to its neighbors in terms of distance, proximity, or closeness.

RF[26] widely uses bagging, random subspace methods, ensemble learning
paradigms. For the purpose of classification decision trees are used. It
decomposes the training dataset set based on certain conditions on attribute
values using random sampling. The data is divided recursively until the leaf
node is left with minimum amount of records using random subset features.

###  3.4 Ensemble Learning Methods

The base estimators are built on a given learning algorithm and their
predictions can be combined to improve the robustness and performance over
single estimator[27]. It includes averaging and boosting methods. In averaging
methods, several estimators are built independently and their predictions are
averaged. The combined estimator performs better due to the reduced variance.
Examples Bagging method, Forests of randomized trees. By contrast, boosting
methods[28], reduces the bias of the combined estimator by building the base
estimators sequentially. Several weak models can be combined to generate a
powerful ensemble. Examples AdaBoost, Gradient Tree Boosting. In this study,
we have considered Bagging method and the rest of the section describes it.

Bagging is also called as Bootstrapped Aggregation[29]. It is used for
predictive modeling (CART). Random subsets of data are drawn from the training
dataset with replacement, and a final model is produced by averaging result
from several models. One popular way of building Bagging Models is by
combining several DecisionTrees with reduced bias that increases the model’s
prediction than individual Decision Trees. Averaging ensembles with bagging
techniques like RandomForestClassifier and ExtraTreesClassifier reduces the
variance, avoids over-fitting and increases the model’s robustness with
respect to small changes in the data.

Figure 1: Proposed Methodology

###  3.5 Result Evaluation Metrics

This section briefly discusses about the metrics used in this study for the
result computation.

Precision: It measures correctness of a classifier. It is the proportion of
number of precisely extracted opinions to the total number of extracted
opinions.

$precision=\frac{TP}{TP+FP}$ (2)

Recall: It measures the sensitivity of a classifier. It is defined as the
proportion of number of precisely extracted opinions to the total number of
annotated opinions.

$recall=\frac{TP}{TP+FN}$ (3)

F-Measure is other commonly used measure. Precision and recall are combined to
give a single criterion called as F-measure. The harmonic mean of precision
and recall computes F measure. It rates a system with one unique rating.

$F-measure=\frac{2*precision*recall}{precision+recall}$ (4)

Accuracy is commonly used performance measure in supervised learning
techniques. It is the ratio of truly predicted observation to the total number
of observations.

$accuracy=\frac{TP+TN}{TP+TN+FP+FN}$ (5)

Here, TP refers to true positive, i.e., the positive tuples that were
faultlessly characterized by the classifier. TN refers to true negative i.e.,
the negative tuples that were faultlessly characterized by the classifier. FP
refers to false positive i.e., the negative tuples that were inaccurately
characterized by the classifier as positive. FN refers to false negative i.e.,
the positive tuples that were untruly characterized by the classifier as
negative.

##  4 Experiments and Results Analysis

The empirical analysis was done to train our model on the Google reviews
dataset and test our model on Utkal university students reviews dataset. The
platform used was Python and the contribution of paper is two-fold. Firstly,
an exploratory analysis on our training dataset i.e., the Google app reviews
and compared the results with the university students dataset through
visualization was done. This helps us to get a glimpse of the behaviour of
students towards the distribution of the app market. Research Questions(RQ)
were investigated to understand the correlation between the price, popularity
and ratings of apps by the students when compared with that of training
dataset. These are presented in Table-II. Secondly, we performed an experiment
to train our model using the classification algorithms on the conventional
text representation schemes. We used evaluation metrics to generate useful
intuitions from our corpus. The three different configurations so obtained are
listed in Table-II and Table-III and the conclusions are as follows:

Table 2: Assessing the Research Questions. Serial |  RQ |  Figure |  Answer
---|---|---|---
RQ1 |  Do the apps which get a higher rating in the training dataset tend to be more popular among the students as well? |  Fig. 2 and and Fig3(b) |  The Google app market breakdown showed prominent downloads in Social and Games categories. On the contrary, Weather and Comics were of least interest among students. The average ratings shooted up to 4.17 across major categories. Interestingly, Shopping, Food and Drinks, News and Magazine are also catching up. Expensive apps may make students disappointed, if they are not good enough and consequently get low ratings. Students from Mathematics and Sanskrit department participated fairly well while Women Studies and Geography showed least participation. Other departments showed an acceptable participation.
RQ2 |  Do the priced and free apps get the same ratings and popularity from the students as compared to the training dataset? |  Fig 3(a) |  This jointplot visualization depicts the sizing strategy(small vs huge). We got a clear conclusion that, small sized app(0-60 Mb) are predominant for downloads among students. This enhances the ratings. Average rating turning out to be 4-5. On the contrary, larger apps have least ratings and less preferable.
RQ3 |  What is the correlation between price, rating, popularity amongst the university students when compared with the training dataset? |  Fig 3(c) |  The installs and reviews are positively correlated amongst students. While, installs and pricing are negatively correlated.
RQ4 |  What is the sentiment polarity could be analyzed by the reviews of students when compared with the training dataset? |  Fig 3(d) |  The scatter plots are heavily clustered towards positive side rather than on negative. Specifically, we can say students weren’t so harsh while give reviews, instead gave genuine and lenient feedback.
RQ5 |  How size of apps affect the installs amongst the students as compared to the training dataset? |  Fig 3(e) |  The points in the jointplot are heavily clustered where the price for apps are 0. This gives us an inference that students prefer free apps rather than paid and an average rating between 3.5 to 5 is shown.
RQ6 |  What confusion matrix can be obtained from students’ reviews as compared to the training dataset? |  Fig 3(f) |  The confusion matrix when applied with LR gave us an accuracy of 90.8%.
Figure 2: Distribution of counts over Category, Ratings, Price

(a) Distribution of Price over Ratings

(b) Distribution of Ratings over Category

(c) Correlation between training and test data set

(d) Distribution of Sentiment Subjectivity over Sentiment Polarity

(e) Distribution of Size over Installs

(f) Confusion Matrix

Figure 3: Visualization of Research Questions Table 3: Accuracy values obtained by Machine Learning algorithms | Uni-gram+TF-IDF | Bi-gram+TF-IDF | Tri-gram+TF-IDF
---|---|---|---
SVM | 92.89 | 93.41 | 93.37
KNN | 91.01 | 90.90 | 88.39
LR | 84.08 | 84.61 | 84.48
RF | 83.42 | 85.11 | 84.16
NB | 80.00 | 82.14 | 82.21
LR(Bagging) | 86.50 | 86.50 | 87.88
NB(Bagging) | 85.50 | 85.11 | 84.00
Table 4: F-values obtained by the Machine Learning algorithms | Uni-gram+TF-IDF | Bi-gram+TF-IDF | Tri-gram+TF-IDF
---|---|---|---
SVM | 0.89 | 0.89 | 0.88
LR | 0.86 | 0.85 | 0.85
KNN | 0.69 | 0.68 | 0.70
RF | 0.68 | 0.61 | 0.62
NB | 0.72 | 0.62 | 0.63
LR(Bagging) | 0.87 | 0.86 | 0.86
NB(Bagging) | 0.75 | 0.76 | 0.76

  * •

Algorithms like SVM and KNN performed best on this dataset on all three
representations uni-gram, bi-gram, tri-gram with TF-IDF featurization. SVM
performed the best in terms of accuracy(93.41%) and F-score(0.89).

  * •

While KNN achieved an accuracy(91.01%) and F-score(0.88) on uni-gram+TF-IDF
featurization.

  * •

LR gave an accuracy of 84.08% and F-score of 0.86 on unigram+TF-IDF scheme. It
is an average algorithm as compared to others and didn’t take a lot of time to
train. It performed fairly well on our dataset when applied with bagging. The
accuracy of 87.8% and F-score of 0.87 so obtained outperformed NB and RF.

  * •

NB proved to be very slow with least accuracy(80%) and F1-score(0.72) amongst
others algorithms and was not that good for this dataset. However, when
applied with Bagging an accuracy(85.50%) and F-score(0.72) was achieved and
outperformed LR and RF.

  * •

RF performed fairly well on our dataset with accuracy(85.11%) and
F-score(0.68) on uni-gram+TF-IDF scheme. It turned out to be an average
algorithm in our study.

Code for the experimental analysis will be available at
https://github.com/smlab-niser/Google-Reviews-Sentiment-Analysis

##  5 Conclusions and Future work

The objective of this paper was to efficiently model the sentiment of the
users using the Google reviews dataset and find the university students’
behavior towards the Google app market. Usually, k-fold cross validation
technique is used for testing. Not much research has been done using students’
reviews for testing. So, we had collected the real-life dataset from Utkal
university students proposed model. Machine learning paradigm was efficiently
employed to perform the sentiment analysis. Five commonly used classification
algorithms namely NB, SVM, KNN, LR, RF were used for performance comparision.
Bagging, an ensemble method, was employed to intensify the predictive
performance of the classifier. In this study, amongst the classification
algorithm, SVM outperformed others in terms of accuracy(93.41%) on the TF-
IDF+bi-gram feature, while NB underperformed with an accuracy(80%). In terms
of F-score, SVM and LR performed significantly well.

Future scope could be, empirical analysis on TP and TF-based representation in
conjugation with uni-gram, bi-gram and tri-gram model respectively. Ensemble
methods like random subspace and boosting can be employed to study the
predictive performance. Analysis of word embeddings(word2vec and GloVe) could
also be explored. Eventually, we could expand our dataset by extending our
online survey in other universities and more students within the range of the
city. Identification of influential reviewers and text summarization could be
one of the grounds for future research.

## Acknowledgement

We would like to dedicate this work of ours to one of our favourite Professor
and former HoD Sir (late) Dr. B.K.Ratha, Department of Computer Science and
Applications, Utkal University. A life full of endless possibilities cut short
in a moment at an age of 54, due to deteriorating health.

He encouraged us all the way and whose encouragement has made sure that ”we
give it all it takes to finish that which we have started”. He is missed
everyday because he has left a void never to be filled in our lives. His care
and concern for us knew no bounds. Our respect for him can never be
quantified. May he find peace and happiness in Paradise!

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