# Semantics between customers and providers:
The relation between product descriptions, reviews,
and customer satisfaction in E-commerce

Carlos A. Rodriguez-Diaz  Sergio Jimenez  Daniel Bejarano  Julio A. Bernal-
Chávez  Alexander Gelbukh  Instituto Caro y Cuervo, Bogotá, Colombia.
Instituto Politécnico Nacional, Centro de Investigación en Computación,
Mexico City, Mexico.

###### Abstract

In social commerce, users dialogue with each other on the topics related to
the providers’ products. However, the language customers use may vary from the
language vendors use on their e-commerce websites and product descriptions.
This situation can lead to possible misunderstandings in the social dialogue
between customers, and incidental costs in the dialogue between customers and
vendors. One possible reason for this difference is that words used by
customers may have different meanings compared to those used by product
description writers. We present a novel approach to measure this potential
lexical-semantic gap for various e-commerce domains using an information-
theoretical approach based on a large corpus of user reviews and product
descriptions. Additionally, we use neural word embeddings to identify words
with the highest semantic drift between reviews and descriptions as a tool to
construct a ground truth for the task. We found that low levels of lexical-
semantic gap are related to better customer satisfaction. Besides, this work
is a step towards a better understanding of the effect of the use of language
in e-commerce and social commerce. Potential applications of this technology
could lead to better communication between customers and improvements in
customer satisfaction, services, and revenue. Furthermore, this study opens up
perspectives for applications in other domains with relationships beyond
client-vendor, such as citizen-government and patient-healthcare system.

###### keywords:

Lexical-semantic gap , customer-vendor language , semantic drift , social
commerce language , product reviews ambiguity , product description ambiguity

##  1 Introduction

E-commerce is an Internet business mediated by a dialogue about products
between suppliers and customers. Initially, this dialogue is one-way, as the
supplier provides the customer with multimedia information about the
description of the products, which customers use to make their purchasing
decisions and set their expectations [1]. In turn, in social e-commerce, users
dialogue with each other to share experiences related to the products and
services they bought, and the topics of conversation are usually based on the
information provided by the supplier [2]. In these dialogues, written language
plays a critical role in product descriptions, user reviews, user-to-user
conversations, and customer service.

The role of language in e-commerce can be compared to _Plain Language_ , an
area dedicated to reduce the linguistic gap between public administration and
citizens [3]. Plain Language problems are very similar to those that arise
between customers and suppliers. First, citizens are a much larger population
than those who write public communications. Second, specialized language is
commonly used since administrators, lawyers, or politicians are in charge of
communicating with citizens. Third, the sociodemographic and language
differences between the two groups greatly influence the effectiveness of the
communication process. The same motivations that make clear language desirable
in public administration may apply to the e-commerce scene.

It is generally assumed that customers fully understand the information
provided by the supplier, but there are several reasons why this might not be
true. First, the user population is generally much larger and more diverse
than the population that writes product descriptions. Second, depending on
each domain, the product descriptions contain specialized language to a
different degree that users may not fully understand [4]. Third, due to the
trend towards globalization of trade, the sociodemographic and linguistic
differences between customers and suppliers can be considerable [5]. These
factors and others, such as the inherently ambiguous nature of the language,
the intensive use of automatic translators [6], among others, suggest that
this problem may significantly affect e-commerce. However, this problem has
received little attention from the scientific community.

There are mainly two situations where this possible gap between the language
of the users and the suppliers generates problematic situations. First, if the
user’s expectations about a product are wrong due to this gap, customer
satisfaction can be compromised. Second, users who interact in social commerce
can reach misunderstandings that threaten the effectiveness and benefits of
that social mechanism. Identifying and characterizing this gap is the first
step to provide possible solutions to alleviate these problematic situations.

From a marketing perspective, the relationship between customer satisfaction
and business benefits has long been established [7] and recently reconfirmed
in e-commerce scenarios [8, 9]. Theories aimed at modeling and explaining
customer satisfaction are mainly based on economic and behavioral factors
related to users and business [10]. On the other hand, empirical studies have
related customer satisfaction with factors such as service quality [11, 12],
logistics [13, 14], and client’s expectation fulfillment [15]. The linguistic
aspects of customer satisfaction in e-commerce have been studied from the
perspective of analyzing the content of user reviews, that is, what words and
expressions represent satisfaction or dissatisfaction in customer discourse
[16]. This user-centric approach is most useful for customer profiling and
product development. However, few studies have studied the language usage from
the side of providers from a holistic perspective and the possible factors
related with customer satisfaction, such as semantics, style, and complexity
[17, 18]. Given that the use of language by customers is only an observable
factor, but the use of language on the provider’s side is a relatively
controllable factor, the possible relationships of the latter with client
satisfaction would open managerial perspectives to improve business profits.

From a linguistic point of view, _semantic drift_ is the change or difference
in the meaning of words between two populations differentiated by usage,
dialect, time, or demography [19]. A word with semantic drift in social
commerce has a predominant sense of use by customers that is different from
its meaning in the textual content produced by the supplier. For example, in
the domain of sports products, for customers, the word “core” refers to a set
of muscles of the human body around the torso, while in product descriptions,
the predominant meaning refers to internal the material or component from
which a particular product is made. While both parties may be aware of the two
meanings, and both may disambiguate depending on the contexts [20], if the
preferred meanings differ significantly, it is plausible that this word is a
factor for misunderstandings. Also, if many words in a domain exhibit this
behavior, the quality of written communication can be compromised.

Manual identification of words with semantic drift in a particular domain is
an overwhelming task for linguists and lexicographers due to the variety and
specificity of the domains and the large number of texts that must be
analyzed. Concordance analysis allows linguists to compare the contexts of use
of a word in two sets of texts. However, even using concordancing software,
since a community’s vocabulary size is often large, detecting words with
semantic drifts is a tedious, overwhelming, and expensive task.

Alternatively, automatic approaches have been studied to identify and measure
semantic drift. These are based on the idea of determining the meaning of the
words by the linguistic distributional hypothesis that states, “you shall know
a word by the company it keeps” (Firth [21]). For this, it is usually required
that each of the speech communities is represented by a corpus of considerable
size to be able to identify statistically significant differences between the
contexts of use of a word [22]. The traditional methods for this are the
vector space model [23], random indexing [24], latent semantic indexing [25],
and others, which have proven to be relatively effective but have limitations
in vocabulary length and in the number of texts that they support. Recently,
there was a paradigm shift in natural language processing towards neural
networks for semantic analysis of words [26]. In particular, _words
embeddings_ [27, 28] make it possible to obtain vector representations for
words in geometric spaces that have semantic properties of similarity,
analogical and compositional reasoning. Word embeddings overcome the
limitations of the previous methods, allowing large amounts of text (billions
of words) to be processed efficiently.

The textual data used in this study seeks to be representative of the
e-commerce and social commerce scenarios. Customer-written product reviews are
the basic mechanism by which the customer community shares information about
their shopping experiences [29]. We will use these reviews assuming that other
types of communication between customers in social commerce have the same
linguistic features. We use the product’s textual descriptions to represent
the suppliers’ language, assuming that they share the same linguistic features
with other textual components in the user interfaces and other communications
between customers and vendors [17].

However, there is a significant limitation in the social commerce scenario for
applying approaches based on word embeddings. While the amount of textual
content users produce in their social interaction is large and suitable for
this approach, the amount of text that describes the products is much smaller.
This imbalance makes the vector representations of the words in the customer
corpus of good quality, while those obtained from the product descriptions
produce noisy vectors.

To address this challenge, in this study, we present a new approach based on
information theory [30] to measure the semantic gap between two corpora highly
unbalanced in size. We hypothesize that by using a commonly used file
compression algorithm (i.e., bzip2) and a randomization mechanism, it is
possible to combine user texts and descriptions so that their compressed sizes
can be used to measure the semantic gap. To assess the validity of this
method, we apply word embeddings to the large corpus representing customers
(product reviews) and the small corpus of product descriptions for a
particular domain. With this, we obtain a set of words with considerable
semantic drift but with a very high error rate due to the marked imbalance in
size. We then manually identify the few actual words with semantic drift in
that set by analyzing the closest neighbor words. These manually selected
words and the magnitude of their semantic drift constitute a ground truth for
evaluating the proposed compression-based method.

Additionally, this new method was tested on a large product description and
review dataset covering 28 product domains. In these experiments, we explore
the possible relationship between the semantic gap and customer satisfaction.

The rest of the article is organized as follows: In Section 2, we present a
review of some related work to the analysis of product reviews and
descriptions, and their relationship to customer satisfaction. Besides, we
provide some background concepts aimed at readers unfamiliar with topics of
computational linguistics to understand the proposed methods and hypothesis
better. In Section 3 we present these methods and the data that were used. In
Section 4, we present the results obtained, and the evaluations carried out.
Then in Section 5, we present the discussion of the results. Finally, in
Section 6, we present the conclusions derived from this study.

##  2 Literature Review

This section seeks to contextualize this study as follows. First, in
subsection 2.1 we review those previous works that have studied the possible
relationships between the textual content of product descriptions and user
reviews, with commercial factors such as customer satisfaction and profit.
Then, in subsections 2.2 and 2.3 we provide contextualization for the methods
presented in Section 3 (Materials and Methods) thus allowing a better
understanding of those methods and the hypotheses of this study. Finally, in
subsection 2.4 such hypothesis are presented.

###  2.1 Related Work

Francis et al. [31] verified in a survey of electronic commerce customers that
the quality of the information in the description of the attributes of the
products positively influences customer satisfaction. In a similar study,
Ludin and Cheng [32] found a significant positive relationship between the
quality of product information and customer satisfaction, which implies a
transitive relationship with e-loyalty. They concluded that the quality of the
information helps customers make better purchasing decisions that,
incidentally, lead to increased customer satisfaction. The recent study by
Patrada and Andajani [33] reconfirms these results, showing the undeniable
effect that the quality of product information has on customer satisfaction.
We assume that among the product information there is always textual content
where quality means a well-written, informative, concise and coherent text.
Mou et al. [34] also found that the quality of written descriptions also has
positive relationships with cognitive involvement, affective product
involvement, and platform customer involvement. In conclusion, the quality of
written content related to products has a positive effect on e-commerce taking
as evidence the opinions of customers.

In the field of artificial intelligence, Pryzant et al. [17] provided evidence
of the close relationship between product descriptions and sales. To do this,
they trained a deep learning neural network from the sequences of words in the
product descriptions to identify the lexical patterns associated with sales.
They found that even controlling for confounding factors like product type,
brand, and price, textual descriptions are highly predictive of sales ($R^{2}$
around 0.80). Although this is not proof of a causal relationship, this
corroborates previous results in the field of marketing, where the positive
effect of the quality of product descriptions is already known. Furthermore,
evidence is added that not only the overall quality but also the lexical and
probably semantic content of these descriptions is responsible for the
possible effects on sales and customer satisfaction.

Other studies have addressed the interaction between product descriptions and
customer reviews. Wang et al. [35] found that for some product characteristics
in the descriptions, it is possible to automatically find related emotional
factors in the reviews to develop products with affective responses. Motivated
by the problem of shortage or poor quality of product descriptions in some
domains, some studies have tackled the task of creating product descriptions
from customer reviews automatically [36, 37, 38]. In addition to solving the
problem posed, the product descriptions turn out to be a reliable content for
customers derived from crowd-sourced material. Other studies have even
proposed the automatic generation of personalized product descriptions in
order to optimize the effect of their textual content on customer satisfaction
[39]. However, although the motivations for these studies are plausible, these
initiatives do not provide empirical evidence that these synthetic product
reviews have an effect on customer satisfaction.

In this context, this study seeks to improve the understanding of the semantic
textual features in product descriptions and their possible effects on
customer satisfaction. In particular, we will study the degree of semantic
alignment between the meanings of words used in product descriptions and in
reviews. This is a meta-linguistic characteristic that is independent of the
used language or its vocabulary. Besides, this feature is associated with a
universal of human language that is ambiguity. In contrast, previous work has
identified particular words in the English language, in particular domains,
that are predictive features of sales or satisfaction. These results are
difficult to generalize to other languages or domains. Our contribution is to
provide a general method to measure the semantic gap between descriptions and
reviews, and thereby empirically test the hypothesis that it is related to
customer satisfaction.

###  2.2 Corpus Information by Compression

Cilibrasi and Vitanyi [40] showed that the Kolmogorov complexity of a data
file, which is the size of its theoretical minimum compressed version, can be
obtained with a normal compressor. Said compressor $C$, should fulfill the
properties of _idempotency_ $C(xx)=C(x)$, _monotonicity_ $C(xy)\geq C(x)$,
_symmetry_ $C(xy)=C(yx)$, y _distributivity_ $C(xy)+C(z)\leq C(xz)+C(yz)$.
Most file compressors (e.g., zip, rar, bzip2, among others) asymptotically
approximate the properties of a normal compressor and hence Kolmogorov
complexity. That way, this notion of information is associated with the size
of the compressed data file, applying in principle to any type of data, but it
is handy for large textual corpora.

A file compressor takes a sequence of numerical data and looks for patterns to
recognize redundancy and somehow “understand” the sequence so that it can be
reproduced again from a condensed version. Thus, a sequence of random numbers
is not compressible, while any sequence with some notion of order is
compressible to some extent. The idea of measuring the information in a corpus
by its compressed size is useful for measuring the amount of information in
some other feature of the corpus. For example, suppose that $x$ is a textual
corpus of English text with $|x|$ bytes in size and $C(x)$ bytes in its
compressed version. Now, let’s create a variant $x^{\prime}$ by replacing all
occurrences of the word “that” with “this”. The uncompressed size $|x|$ is
equal to $|x^{\prime}|$, but $C(x)>C(x^{\prime})$ because $x$ has more
information than $x^{\prime}$ because it has one less word in its vocabulary.
The difference $\delta=C(x)-C(x^{\prime})$ represents the amount of
information that conveys the word “that” in corpus $x$, which is the feature
to be measured in this example.

Montemurro and Zanette [41] used the same compression principle to measure the
amount of information associated with the order of words in a corpus. They
produced a variant of an original corpus by shuffling the words in each
sentence, making two corpora of identical size. The difference between the
compressed sizes of these two corpora represents the amount of information
associated with the order of the words. They found that this difference is
universal in different language families. Similarly, the method presented in
this study manipulates the text corpora produced by the speaking communities
of customers and providers in such a way that the difference in size between
the two versions of the corpora represents the semantic gap between the two
communities.

###  2.3 Measuring Word Drifts by Word Embeddings

Neural word embeddings are a set of methods for obtaining vector
representations for words in a high-dimensional Euclidean space. The input is
a large textual corpus; the output is a $n\times m$ matrix, where each row is
a vector of dimension $m$ associated with each of the $n$ words in the
vocabulary of the corpus. Thus, each word is located in some coordinates in
the space of $m$-dimensions, where its position has semantic properties. The
main property of that space is that the distance between the words represents
a value of similarity or semantic relationship. Therefore, two words like
“car” and “automobile” should be close, while “car” and “cucumber” should be
far from each other. Compositionality is another property of said semantic
space, so the vector sum of the representations of, say, “doctor” and “heart”
must be close to “cardiologist”. Also, this space allows for analog reasoning.
For example, the relative distance and direction between the words “king” and
“queen” are the same as those between “prince” and “princess”. This means that
it is possible to solve analogies like “king”$\rightarrow$“queen”:
“prince”$\rightarrow X$. These word representations are obtained from the
weights of the links between neurons in an artificial neural network trained
in a word co-occurrence prediction task using a large corpus as a data set for
training.

The semantic drifts of words can be determined by geometrically comparing the
vectorial representation of the words obtained using two large corpora
representing each community of speech or dialect. The detection of word
drifts, as well as the complementary task of finding semantic equivalences,
can be performed using word embeddings [42]. For these tasks, the input is two
corpora, each representing a community of speech or dialect, and the output is
a single semantic Euclidean space where the words to be tested are labeled
with an identifier of their source corpus. The distances between pairs of
labeled words in such space are compared. Words that are close to its pair are
considered semantic equivalences, and the farthest pairs are considered
semantic drifts. Approaches for obtaining such single semantic space from two
corpora range from geometric alignment [43], conditional probability [44, 45],
and meta-algorithms on current word embedding algorithms [46, 47].

To evaluate the quality of the results of the words identified with semantic
drift, it is necessary to have a gold standard, which generally must be
constructed manually. Professional linguists typically perform this task and
usually produces few instances [48]. For example, in the DIACR-Ita challenge
[49], there were only 18 words available in the gold standard to detect
semantic drift in a diachronic corpus (that is, a corpus from the past and one
from the present). In this challenge, Angel et al. [50] showed that it is not
necessary to obtain a single semantic space to identify semantic drifts but
that this can be done using the word embeddings obtained separately for each
corpus. To identify if a word has semantic drift, its corresponding sets of
$k$ neighboring words in each semantic space are collected and compared with
the Jaccard index. The target words that get the lowest value of this index
have the highest semantic drift. This approach obtained good results, and it
was robust to the imbalance between corpus’ sizes with few texts from the past
and many texts from the present. In the method presented in this study, we
will use that idea to alleviate the problem of imbalance between the amounts
of text produced by customers compared to product descriptions.

###  2.4 Research Questions and Hypothesis

This study aims to propose an adequate way to measure the semantic gap between
the language of customers and suppliers in an e-commerce scenario, and to
identify whether such a gap exists and what is its magnitude for different
market domains. In addition, it seeks to determine whether this situation is
related to customer satisfaction.

The hypotheses that we seek to test in an e-commerce environment, where user
reviews and product descriptions are available, are the following:

  * 1.

$H_{1}$: If there are differences in the word usage contexts (i.e., senses)
between reviews and descriptions, then the difference in the amount of
information between them due to that (information theoretically speaking) is a
measure of the magnitude of the semantic gap between customers and suppliers.

  * 2.

To operationalize the $H_{1}$ hypothesis in the proposed theoretical
framework, we propose the following null auxiliary hypothesis. Assume that you
have two corpora: the first (called $True$) is a combination of revisions and
descriptions, and the second (called $Rand$) is a copy of $True$ to which
random changes are introduced in the contexts of use of words.

$H_{0}^{aux}$: If there is no size difference between the compressed files of
$True$ and $Rand$, then there are no differences between the senses of use of
the words in reviews and descriptions.

  * 3.

$H_{2}$: The greater the lexical-semantic gap between customers and providers,
the lower the customer satisfaction.

##  3 Materials and Methods

The methodology presented in this section aims to address two objectives.
First, present a method to measure the degree of semantic gap between the
language of customers and suppliers. Second, provide the framework for
checking the hypothesis whether the magnitude of this gap is related to
customer satisfaction in social e-commerce environments. These objectives
require a considerable amount of representative texts, where a domain level
categorization is available, to be able to observe and analyze the
plausibility of possible variations. In addition, to validate the hypothesis,
it is necessary to have associated metadata that provide quantitative
information on customer satisfaction (for example, ratings given by
customers). The main methodological challenge is the design of a semantic gap
measure in highly unbalanced data such as the huge textual output of customers
in the form of product reviews, in contrast to the few quantities of product
descriptions available. Since the known methods for this (i.e. neural word
embeddings) require a fairly balanced corpus, we propose a measure of the
semantic gap based on information theory. To validate this method, we applied
neural word embeddings, which are widely recognized for their suitability for
the task, on the unbalanced data and performed manual curation of the results
to build a gold standard for the task. Thus, when comparing this gold standard
with the results of the proposed method, a completely automatic method for the
task would be validated and obtained. Finally, the gap measures will be
analyzed qualitatively and then quantitatively compared to customer
satisfaction data.

###  3.1 The Data

We used the _Amazon Review Data 2018_ [51] that contains around 180 million of
reviews and 12 million product descriptions distributed along 28 domains in
power-law like. We used the text of the reviews and extracted text from the
product data fields _title_ , _tech1_ , _description_ , _feature_ , and
_similar item_. Each of the reviews contains a rating on a discrete scale
between 5 (best) to 1 (worst) that we will use as a quantitative indicator of
customer satisfaction. Sentence boundaries were identified using the
NLTK111Natural Language Toolkit at https://www.nltk.org/, and rows from
tabular descriptions were considered as “sentences”. Next, all non-
alphabetical tokens were removed. The sizes of the resulting review and
description corpora per domain are shown in Table 1.

Table 1:  Number of words for each domain and word count ratio between reviews and descriptions Category | Reviews’ words | Descriptions’ words |  Word imbalance ratio
---|---|---|---
All Beauty | 13,454,743 | 1,285,534 |  10:1
Amazon Fashion | 22,235,491 | 5,919,402 |  4:1
Appliances | 19,211,478 | 4,566,216 |  4:1
Arts Crafts & Sewing | 74,693,032 | 27,578,534 |  3:1
Automotive | 226,834,970 | 161,065,374 |  1:1
CDs & Vinyl | 386,158,660 | 21,973,448 |  18:1
Cell Phones & Accessories | 349,903,420 | 72,093,498 |  5:1
Clothing Shoes & Jewelry | 866,541,485 | 329,871,943 |  3:1
Digital Music | 55,810,053 | 1,554,864 |  36:1
Electronics | 1,024,197,307 | 135,120,799 |  8:1
Gift Cards | 2,372,944 | 84,021 |  28:1
Grocery & Gourmet Food | 149,018,135 | 26,804,856 |  6:1
Home & Kitchen | 759,164,935 | 192,811,582 |  4:1
Industrial & Scientific | 54,799,737 | 16,627,174 |  3:1
Kindle Store | 382,079,930 | 3,420,666 |  112:1
Luxury Beauty | 21,696,560 | 1,040,486 |  21:1
Magazine Subscriptions | 3,610,776 | 133,637 |  27:1
Movies & TV | 469,363,734 | 18,061,999 |  26:1
Musical Instruments | 69,072,246 | 18,739,313 |  4:1
Office Products | 191,650,532 | 49,943,042 |  4:1
Patio Lawn & Garden | 186,284,611 | 45,539,118 |  4:1
Pet Supplies | 269,510,943 | 32,330,942 |  8:1
Prime Pantry | 10,415,289 | 1,048,980 |  10:1
Software | 33,571,934 | 4,916,812 |  7:1
Sports & Outdoors | 468,858,744 | 91,971,650 |  5:1
Tools & Home Improvement | 327,711,025 | 107,013,154 |  3:1
Toys & Games | 273,351,609 | 82,249,226 |  3:1
Video Games | 179,005,493 | 11,711,784 |  15:1

###  3.2 Semantic Gap by Compression

Inspired by Montemurro and Zanette [41], for each domain, we produced two
versions of the same size of the combined reviews and description corpora, one
with the original sentences (_True_) and the other with random word shifts
(_Rand_). These shifts are designed so that the difference between the
compressed sizes of the two corpora may reflect the semantic gap between
customers and product description writers. In computational linguistics, two
words can be considered synonyms if they are interchangeable in many contexts
[52]. The _Rand_ corpus represents the null hypothesis that a particular word
$w$ (or a set of words) has the same (or close) meaning in reviews and
descriptions because random exchanges should not affect the null hypothesis of
interchangeability. If there is little or no difference in the compressed
sizes, the null hypothesis is tested.

First, for each domain, we create a mixed corpus of reviews and descriptions
by inserting a sentence from each source one at a time. The motivation for
this is that most current compressors are stream-based, so they would
incrementally encode the reviews and descriptions data, favoring the
distributivity property of compression (see subsection 2.2). Since the
sentences in the corpus of reviews are more abundant than in the corpus of
descriptions, we start over with the first description as many times as
necessary once we run out of descriptions. Next, we select the vocabulary $W$
to be tested for drift, removing from the common vocabulary between reviews
and descriptions the 500 most frequent words and the words that appear less
than 50 times. The motivation for this is that the most frequent words are
usually functional words that have little or no ambiguity. Similarly, at the
other end of the frequency spectrum, words with few occurrences provide little
evidence for automatic analysis.

Then for each word $w$ in $W$, we label each occurrence $w_{R}$ if $w$ occurs
in a review and $w_{D}$ for a description. We call this the corpus _True_. To
produce the corpus _Rand_ , we randomly swap the labels $R$ and $D$ on each
target word with a probability $P=0.5$ (i.e., by “flipping a coin”). The
justification for this value came from the analysis of the extreme values
$P=0$, which means that there are no changes, and $P=1$, which means a tag
exchange that does not produce changes either. The motivation for this
randomization is that if $w$ has the same meaning in reviews and descriptions,
then $w_{R}$ and $w_{D}$ can be swapped (i.e., interchanged in any context).
On the contrary, if $w_{R}$ and $w_{D}$ have different meanings, the random
exchange would place words in unexpected contexts that would increase the
corpus’s general information. Figure 1 illustrates this process in a graphic
analogy: gray represents text for reviews, blue for descriptions, horizontal
stripes represent sentences, small squares are target words, and the fill
color of these squares are their labels. Finally, we measure the size of the
two compressed corpora using the _bzip2_ 222http://www.bzip.org/ compressor,
the same option that Wikipedia administrators use to store and distribute the
backups of their encyclopedia.

Figure 1: The process to build the _True_ and _Rand_ corpus (of equal size)
from the product reviews and descriptions

Table 2 shows an example of the _True_ and _Rand_ corpora for $w$ =“nature” in
the _Electronics_ domain. These corpora are the result of the successive
alternation of a sentence of revisions and a sentence of descriptions. In the
_True_ corpus, the tags of the target word “ nature ” match the source corpus
of the sentence, while in the _Rand_ corpus the tags are randomly assigned. If
the target word is used in the same sense in both corpora, most contexts would
be similar to each other, and there would not be a considerable difference
between _True_ and _Rand_. Otherwise, the contexts would differ, and the
random shifts would result in the tagged target word being placed in many
inappropriate contexts, thus increasing the amount of information in the
_Rand_ corpus. In the example in Table 2, only a single target word is
considered, but this tagging process is done simultaneously for all target
words, those that are neither very frequent nor very rare in the corpus. In
particular, the word “nature” exhibits a drift in the _Electronics_ domain.
The dominant meaning in descriptions is “the outer world of living beings”,
while in reviews, it is “the basic character or quality”. However, in other
domains such as _CD & Vinyl_, “nature” is related both in reviews and
descriptions to idealism, spirituality, and serenity.

Table 2: Example of the _True_ and _Rand_ mixed reviews/descriptions corpora
for the sample word “nature”. A R label is attached to target words from the
corpus of reviews and D from descriptions.
True Label |  True Corpus |  Rand. Label |  Random Corpus
---|---|---|---
R |  That is the nature_R of digital. |  D |  That is the nature_D of digital.
D |  Improve your mood in nature_D euphony. |  D |  Improve your mood in nature_D euphony.
R |  The nature_R of wireless is noise. |  R |  The nature_R of wireless is noise.
D |  Bushnell nature_D view plus x compact binocular. |  R |  Bushnell nature_R view plus x compact binocular.
R |  Just the nature_R of the beast. |  D |  Just the nature_D of the beast.
D |  Timex cd clock radio with nature_D sounds |  R |  Timex cd clock radio with nature_R sounds.
R |  Love the retractable nature_R of this cord. |  R |  Love the retractable nature_R of this cord.
D |  Separate nature_D pictures for each individual verse. |  R |  Separate nature_R pictures for each individual verse.
R |  I rarely use the nature_R sounds. |  D |  I rarely use the nature_D sounds.
D |  Used and praised by nature_D photographers. |  R |  Used and praised by nature_R photographers.
R |  That is just the nature_R of privacy filters. |  D |  That is just the nature_D of privacy filters.
D |  Perfect for nature_D sports and surveillance photos. |  D |  Perfect for nature_D sports and surveillance photos.

###  3.3 Word Embeddings for Word Drift

To detect words with different meanings between reviews and description
corpora for each domain, we followed Angel et al. [50] by training word
embeddings for each corpus and comparing neighboring words for each target
word. We use the implementation of _word2vec_ [27] in Gensim [53] using the
CBOW algorithm and a 5-word window (these are the default parameters). Since
for all domains, each corpus of reviews is larger than its corresponding
corpus of descriptions; we selected a vector size of 200 for reviews and 50
for descriptions to reduce the total number of parameters to learn for the
embeddings from descriptions. Additionally, we only consider words that occur
at least 50 times in the corpus of reviews and ten times in descriptions.
Finally, two sets of word embeddings were obtained for each domain by training
for five epochs (i.e., passes of the corpus) of the reviews corpus and ten
epochs of the corpus of descriptions (larger vector sizes usually should
require more epochs).

Next, for each word in common between the two word embeddings for a domain, we
obtained the $k=30$ closest neighbors in reviews and descriptions. All words
in common were scored with the following expression:

$S(w)=\log(\min(f_{w_{r}},f_{w_{d}}))\times(1-Jaccard(w_{r},w_{d}))^{p}$ (1)

Here, $f_{w_{r}}$ is the frequency of the word $w$ in the reviews corpus and
$f_{w_{d}}$ in the descriptions, $w_{r}$ is the set of the closest neighboring
words of $w$ in the reviews’ word embeddings and $w_{d}$ in descriptions,
$J()$ is the Jaccard coefficient, and $p$ is a parameter for the
multiplicative combination set to 5. In computational linguistics it is used
to logarithmically scale the frequencies of the words in a corpus due to
Zipf’s Law. This scoring function aims to select the most frequent words in
both corpora with few neighboring words in common. It should be noted that the
frequencies $f_{w_{r}}$ and $f_{w_{d}}$ are obtained from the artificially
balanced corpus (_True_) and not from the original corpus. That is, their
values are comparable.

###  3.4 Building a ground truth for the word drift task

To provide the initial ground truth for finding the words with the highest
semantic drift between reviews and descriptions, we rank the words in each
domain according to $S()$ (eq. 1) in descending order. Next, we identify the
first ten words with semantic drift by manually analyzing the two neighboring
word lists. The domains _Gift Cards_ and _Magazine Subscriptions_ were ignored
due to the poor quality of their word embeddings from descriptions due to
their small corpus size. However, we noticed that the semantic relationship of
the $k$-nearest neighboring words is better in the word embeddings obtained
from product reviews than that of descriptions. Many unrelated words had to be
discarded from the set of neighbor words from descriptions to discern the
meaning used in that corpus. A professional linguist performed this task. For
most of the domains, the first ten words with a clear word drift were found
among the first 200 words ordered by $S()$, thus obtaining 280 instances in
the ground truth. Table 3 shows examples of these words for some domains. The
entire manually created ground truth is available at
https://tinyurl.com/mhk5a4d4.

Table 3: Examples of words exhibiting semantic drifts obtained from word embeddings trained separately on reviews and descriptions  Domain | Word |  Neighbors in Reviews |  Neighbors in Descriptions
---|---|---|---
All Beauty | sexy |  feminine classy elegant girly manly sparkly |  lingerie tights underwear sleepwear lolita
Arts Crafts | friendly |  efficient intuitive satisfying responsive |  safe conscious biodegradable recyclable
Automotive | cool |  badass sexy sweet cute nice snazzy neat classy |  warm toasty refreshing cockpit blazing cozy
Arts Crafts & Sewing | hands |  fingers finger wrist thumb fingertip |  professionalism expertly handwash machine tlc
CDs & Vinyl | lovers |  enthusiast connoisseur fanatic fan collector |  woman man lady fool baby letter girl kisses
Cell Phones & Accessories | hard |  difficult harder tough easy tricky |  tpu silicone silicon hardshell tortoise
Clothing Shoes & Jewelry | left |  leaving cracked rubbed leaves corner |  click sizing exploded front length forearm
Digital Music | number |  handful slew couple several consecutive |  top labels period shelf seam
Electronics | bus |  buses train plane subway trains |  nics uart quatech epp fsb
Kindle Store | love |  adore loved enjoy loving loves |  passion hope true trusts
Figure 2: Measures of the semantic gap between the language used in reviews
and product descriptions. Greater intensity of the red and greater size of the
mark indicate greater semantic gap. Greater intensity of the blue and greater
size of the mark indicate a smaller semantic gap.

##  4 Results

Here we present the results of applying the method to measure the semantic gap
(subsection 3.2) in each of the 28 domains of the Amazon dataset. Although the
validity of the presented method is theoretically based on the distributional
hypothesis for word semantics and on information theory, the experiments seek
to provide additional experimental validation. For this we use the word
embedding method (see subsection 2.3), which is already known to be suitable
for the task of measuring semantic distances between words, provided that two
large and balanced corpora are available. As Table 1 shows, the data set has
large differences between the sizes of each corpus on each domain, as well as
the size imbalance between reviews and descriptions. Consequently, the likely
poor results obtained from these small and highly unbalanced corpus using word
embeddings will be manually curated to select only quality word drifts for
valitating our semantic gap method. The results of this experiment provide the
information to test $H_{1}$. For the $H_{2}$ test, the semantic gap measures
for each domain are compared to the average of the numerical ratings
associated with each product review for each domain.

Figure 2 shows the relative differences obtained between the corpus _True_ and
_Rand_ for each domain plotted against the compression ratio of _True_. Since
these differences are correlated with the compression ratio, the deviations
from the trend line reflect the magnitude of the semantic gap between reviews
and descriptions. Table 4 reports the magnitude of these deviations in the
column “Gap score”, which represents our function to measure the semantic gap,
also used to order the domains in Table 4.

To check the validity of the variable “Gap score”, we use the ground truth
described in subsection 3.4. For each domain, we average the Jaccard scores
(from eq. 1) for the leading words according to $S()$ until all ten words in
the ground truth are retrieved. These values are reported in Table 4 in the
column labeled “Avg.J@10”. We compare this with the “Gap score” and carefully
check for other potential confounders, such as the size of the corpus of
reviews or descriptions and their differences, to avoid a spurious
correlation. Since “Avg.J@10” failed by far the D’Agostino and Pearson
[54]333We used this implementation of the test:
https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.normaltest.html
normality test ($p=4.5\times 10^{-8}$), we opted for the Spearman correlation
as a non-parametric alternative obtaining $\rho=0.49$, $n=26$ ($p<0.01)$. This
result proves the hypothesis $H_{1}$. Regarding $H_{0}^{aux}$, we do not find
any supporting evidence because all differences in the sizes of the compressed
files $\delta$ for each domain were positive and the expert annotator found
valid word drifts in all domains. This means that all studied domains exhibits
some degree of semantical gap, leading us to reject $H_{0}^{aux}$ according to
the available data.

To evaluate the relationship between the “ Gap score ” and customer
satisfaction, we obtained the average of the product ratings on a 5-starts
scale associated with the reviews having the attribute “verified” in the
dataset for each domain. For this, we report in Table 4 the number of verified
reviews along with the average rating given by customers along with their
standard deviation. The independent variable “Gap Score” is continuous and
normally distributed, since it passed the D’Agostino and Pearson [54] test
($p=0.052>0.05$). Similarly, the dependent variable “Avg. Rating” is
continuous, since it is the average of numerous discrete ratings in each
domain, and it is also normally distributed as demonstrated by the same test
($p=0.257>0.05$). Thus, the Pearson correlation seems appropriate to measure
the relationship between those variables, obtaining $r=0.32$ for $n=28$
($p<0.05$). This result tests the hypothesis $H_{2}$. As the normality test of
the “Gap score” variable is close to the critical value, we also considered
the Spearman correlation, obtaining $\rho=0.28$, ($p<0.10)$. Although with
less statistical significance, this result reconfirms the $H_{2}$ test.

Table 4:  Results of our semantic gap function along with the average rating (standard deviation in parentheses) and average Jaccard index for the words with the largest drift (until the 10th ground truth word was retrieved) Category domain | Gap rank | Gap Score | Verif. Rev. | Avg. rating | Avg.J@10
---|---|---|---|---|---
Clothing Shoes & Jewelry | 1 | 0.0151 | 30,286,706 | 4.19(1.23) | 0.7306
Industrial & Scientific | 2 | 0.0124 | 1,636,564 | 4.32(1.22) | 0.6529
Sports & Outdoors | 3 | 0.0115 | 11,913,059 | 4.26(1.23) | 0.4781
Arts Crafts & Sewing | 4 | 0.0107 | 2,682,955 | 4.33(1.20) | 3.2946
Prime Pantry | 5 | 0.0099 | 382,686 | 4.38(1.15) | 1.1008
Pet Supplies | 6 | 0.0083 | 5,979,680 | 4.17(1.32) | 1.0101
Luxury Beauty | 7 | 0.0082 | 504,542 | 4.25(1.29) | 2.1483
Home & Kitchen | 8 | 0.0064 | 20,015,942 | 4.22(1.29) | 0.7359
Electronics | 9 | 0.0058 | 18,597,092 | 4.11(1.36) | 1.3168
Toys & Games | 10 | 0.0033 | 7,231,522 | 4.26(1.25) | 0.6669
Patio Lawn & Garden | 11 | 0.0032 | 4,799,516 | 4.15(1.35) | 0.9936
Video Games | 12 | 0.0025 | 1,948,309 | 4.10(1.37) | 0.2566
Tools & Home Improvement | 13 | 0.0018 | 8,299,068 | 4.25(1.27) | 0.5611
All Beauty | 14 | 0.0011 | 322,473 | 4.11(1.36) | 1.5279
Software | 15 | 0.0000 | 309,345 | 3.75(1.56) | 2.0888
Musical Instruments | 16 | -0.0005 | 1,346,124 | 4.28(1.22) | 0.6349
Cell Phones & Accessories | 17 | -0.0008 | 9,209,864 | 3.94(1.46) | 2.2113
Amazon Fashion | 18 | -0.0016 | 828,699 | 3.90(1.42) | 2.5973
Office Products | 19 | -0.0020 | 5,0551,52 | 4.22(1.30) | 1.0930
Automotive | 20 | -0.0021 | 7,561,414 | 4.27(1.27) | 1.0790
Digital Music | 21 | -0.0033 | 1,217,667 | 4.69(0.79) | 1.9304
Movies & TV | 22 | -0.0047 | 6,731,296 | 4.31(1.17) | 1.5674
Kindle Store | 23 | -0.0060 | 4,036,164 | 4.28(1.11) | 1.9401
CDs & Vinyl | 24 | -0.0085 | 2,578,257 | 4.60(0.88) | 2.6809
Grocery & Gourmet Food | 25 | -0.0114 | 4,437,360 | 4.35(1.21) | 1.8710
Magazine Subscriptions | 26 | -0.0164 | 58,654 | 4.21(1.29) | n.a.
Gift Cards | 27 | -0.0233 | 138,237 | 4.71(0.88) | n.a.
Appliances | 28 | -0.0262 | 563,870 | 4.35(1.23) | 6.8799

##  5 Discussion

In the results obtained, we observe that domains whose items are related in
some way to the language got a small gap, e.g., _Magazine Subscriptions_ ,
_CDs & Vinyl_, _Kindle Store_ , and _Movies & TV_. Not surprisingly, the
_Appliances_ domain, an industry with a long tradition of user-friendly
product descriptions, has the smallest semantic gap, implying well-aligned
language between customers and suppliers. In contrast, more technical domains
such as _Industrial & Scientific_, _Electronics_ , and _Sports & Outdoors_ had
more significant semantic gaps. However, other technical domains such as
_Software_ and _Video Games_ obtained an intermediate gap, showing a better
alignment in the customers’ language and that of the descriptions writers.
Although the relationship between customer satisfaction and the quality of the
descriptions has already been widely supported in the related literature [55,
56, 57, 58], this result shows that the quality of the information is not the
only factor, but rather that the use of a language semantically close to that
of customers is also important.

In general, we consider that the semantic gaps obtained for each domain are
correct, which shows that the method proposed in subsection 3.2 is a plausible
approximation to the problem. These observations also provide evidence that
the information-theoretical approach is sound and that a commonly used file
compressor, such as _bzip2_ , is a suitable tool for the task.

The observed correlation between the semantic gap and the average review
ratings is essential because ratings can be associated with overall customer
satisfaction [59]. Thus, the average customer ratings reflect their level of
satisfaction, whether it be for receiving better recommendations, exceeding
expectations, convenient user interface, product quality, descriptions
quality, or other factors. Our findings show that one of these factors may be
the narrow semantic gap between the language of the customer community and the
textual content produced by the provider. It is well known that customer
satisfaction has a favorable impact on e-commerce trust [11]. If this
relationship can be extended to this linguistic factor, which has the
potential to be controllable, it opens up a clear perspective for improving
customers’ shopping experience and, ultimately, business profits. This new
relationship between the linguistic-semantic alignment of customer-provider
communication and customer satisfaction, along with the considerable variation
observed in this linguistic factor across different domains, provides tools
for managers to make differential, revenue-weighted intervention decisions.
Managerial decisions aimed at improving customer satisfaction are generally
differentiated, for example, in the types of customers, in order to optimize
the resources invested [60]. The semantic gap measure provides the information
necessary to support these decisions, either for different domains or for any
other categorization that can be performed in the input corpus. The possible
measures that can be applied to reduce the semantic gap could be to follow the
recommendations of the ”plain language” movement [61], mainly taking into
account the intended audience of each document directed to the customers that
the organization produces. From a theoretical point of view, the discovery of
the direct relationship between the absence of semantic gap and customer
satisfaction provides an important resource for developing behavior models,
where the use of language by customers is an independent and observable
variable, while on the supplier side it is controllable.

Regarding the relationship between this semantic gap and the quality of
customer-to-customer communication in social commerce, our results do not
provide direct evidence of its existence. However, it is plausible to infer
that customer satisfaction is also related to better communication due to less
ambiguous language.

Regarding the observed relationship between our semantic gap function and
human judgments of the ground truth (“Avg.J@10” in Table 4), this is a
quantitative indication that the information-theoretical method presented
measures the semantic gap due to word drift and ambiguity. It can be concluded
that the method manages to effectively measure the semantic gap between
customers and suppliers in e-commerce, but has the limitation that it does not
identify particular words. However, the manually extracted ground truth is the
necessary resource for developing and evaluating the methods to identify these
words.

One of the advantages of the proposed method for measuring the semantic gap
between customers and suppliers is that it is not based on particular features
of the English language. Therefore, it can be applied in any language where
the speech can be segmented into words. Regarding the required size of the
corpus of reviews and descriptions, the Amazon data provided a wide range of
sizes, but even the smallest corpus (see Table 1) can be considered large
compared to those that can be collected from other e-commerce sites or from
other smaller domains. Although we do not provide evidence of the reliability
of the semantic gap measurements for small corpus, we consider that a corpus
of reviews with more than 1 million words and a corpus of product descriptions
with at least 100,000 words for each domain should be sufficient for a
reliable measurement.

##  6 Conclusions, Limitations, and Implications

We present a new method motivated by distributional semantics and information
theory based on file compression to measure the lexical-semantic gap between
product reviews and descriptions for various e-commerce domains. The proposed
computational linguistics method makes very few assumptions, has no
parameters, is language independent, and it do not require manual annotations
or labor. It is based on Shannon’s information theory and draws inspiration
from other applications where this approach has been effective. In addition,
by using word embeddings, we build a ground truth for the task of identifying
words with semantic drift (i.e., different meanings) between product reviews
and descriptions. This ground truth served to validate the soundness of the
presented method. To obtain this gold standard, we applied a state-of-the-art
natural language processing method (i.e. neural word embeddings) that produced
a result that was selected manually by a professional linguist. This new
resource provided additional validation, since the semantic gap measures for
each domain ($n=26$) were highly correlated ($r=0.67$) with the average
semantic drifts of the words in the semi-automatically obtained model.
Furthermore, this gold standard constitutes a new resource for future research
on this phenomenon.

The most important observation of this study is that the semantic gap of
various e-commerce domains has an inverse relationship with the quantitative
ratings (on a 5-star scale) that customers give to the products purchased.
This implies that the semantic differences in the language of the customers
and suppliers have a relationship with customer satisfaction. Thus, the
greater the semantic gap, the lower the customer satisfaction, represented by
their ratings. This relationship was observed using the proposed method in a
large corpus consisting of 6.9 billion words of user reviews and 1.5 billion
words of product descriptions. This corpus came from Amazon and is distributed
across 28 marketing domains. The observed correlation between the semantic gap
with customer satisfaction represented by the average of the verified ratings
was $r=0.32$ ($p\leq 0.05$). This data is probably the largest and most
representative social e-commerce data publicly available.

###  6.1 Theoretical Contribution

Customer satisfaction in an e-commerce environment is a widely studied
multifactorial phenomenon, but with few studies related to language. In social
e-commerce, language plays a more prominent role than in classic e-commerce
environments due to the written dialogue between customers. The differences in
the meaning of words in the discourse of customers and suppliers, what we have
called the _semantic gap_ , is a new factor that we show and that can be
effectively measured even with the limitations of the scenario. The motivation
for revealing this non-superficial linguistic feature is that semantics is
probably the main factor associated with the effectiveness of human
communication. We have found that the semantic gap is negatively related to
customer satisfaction. This means that language not only has a functional-
utilitarian role in the customer-provider relationship, but it also affects
the quality of this relationship with its known implications for the business.

The semantic gap is a new variable that adds to those already known as
explanatory of the phenomenon of customer satisfaction such as logistics
quality, product quality, customer service, personalization, etc. This new
variable can contribute to improving the predictive capacity of customer
satisfaction models, since it belongs to a domain external to management, and
that is generally not considered in current theory.

This contribution is of practical use in any social e-commerce environment
where there is considerable availability of customer-written texts (e.g.,
reviews) and textual descriptions of products. In this scenario, the semantic
gap provides a quantitative measure to identify subsets of customers with low
satisfaction due to this linguistic difference.

###  6.2 Limitations

From the linguistic point of view, the method presented to measure the
semantic gap has very few limitations to be applied in other types of texts or
in other languages. This is because, in practice, it is only required to be
able to segment texts at the word level, perform random manipulations at this
level, and then use a standard file compressor. In agglutinative languages
(e.g. Finnish) it may be necessary to perform word segmentation to control
excessive vocabulary size. With regard to other types of documents to
represent users (e.g. complaints and requests) and providers (e.g. manuals and
regulations), it is reasonable to consider that $H_{1}$ may also be compatible
with these types of data. Similarly, the semantic gap method could also be
used with transcripts of dialogues in oral modality.

A limitation of this study is that its applicability is clear for large retail
companies that have a considerable number of domains to compare. In e-commerce
based on a single domain, the semantic gap measure loses its usefulness if it
is not comparable with other domains, since it is relative to factors such as
the size of the corpus and the compression ratio. However, relative semantic
gap measures could be used in customer groupings other than by product domain,
such as demographic or behavioral factors, among others.

Although it is plausible and appealing to generalize that a small semantic
gap, which may imply good communication, could be positively related to
customer satisfaction, our results show that this relationship, although
statistically significant, is relatively weak. Consequently, this relationship
should be considered with caution in other domains not very similar to those
studied, such as finance, real estate, among others. Similarly, the possible
causality from semantic gap to customer satisfaction seems reasonable, because
of the apparent impossibility of an implication in the opposite direction.
However, this consideration should be done with caution since our results
outline possible confounding factors such as the degree to which language is
involved with the product, as in the case of magazines and movies.

###  6.3 Implications

The main contributions and implications of this study can be summarized as:

  1. 1.

For managers: For different marketing domains, there are varying degrees of
differences in meaning between the words used by customers and those used in
product descriptions (i.e. semantic gap). This difference is related to
customer satisfaction: the smaller the semantic gap, the higher the customer
satisfaction. This result was obtained without assumptions or information
different from the linguistic data associated with each domain, so it is
reasonable that it can be transferred beyond the 28 Amazon domains studied. To
measure such a gap, it is only necessary to have a relatively large corpus of
texts produced by customers (for example, product reviews) and another corpus
with product descriptions. These two corpuses are compared by a simple
preprocessing (accessible to any professional computer programmer) and with
the use of a standard file compressor such as bzip2. Any action in the written
discourse in the product descriptions aimed at reducing the semantic gap is
expected to have a beneficial effect on customer satisfaction and ultimately
on business profits.

  2. 2.

For academicians: The relationship between the quality of information in
product descriptions and customer satisfaction is well known. This study has
specified the generality of the concept of ”quality of information” to that of
”quality of communication” between customers and suppliers when product
descriptions are used as a communication channel. Here, the quality of
communication refers to a low degree of ambiguity in the common terminology
used by customers and suppliers.

  3. 3.

For computational linguists: The method to measure the semantic gap between
two corpus by compression is a new approach that has proven particularly
useful in cases where there is a large imbalance between the size of the
corpus to be compared. The method can be easily adapted to measure the
semantic gap in subsets of vocabulary words, even to measure the semantic
drift of individual words. In addition, this method obtained results similar
to those that can be obtained when comparing distances using neural word
embeddings.

###  6.4 Future Work

Several research perspectives are opened from the methods and resources
presented in this study:

  1. 1.

It is interesting to investigate how the lexical-semantic gap affects other
factors of e-commerce, such as customer service, trust, loyalty, and others.

  2. 2.

It is also interesting to study whether the semantic gap also affects
customer-customer communications in social commerce and its implications.

  3. 3.

The users’ reviews have other features in addition to their sentiment polarity
(i.e., rating), such as intensity and usefulness, that can serve to explore
the semantic gap and thus expand the understanding of it and its relationships
with other factors of marketing.

  4. 4.

Research on the possible transfer of the methods presented to other analogous
domains, such as those of the citizen-government or patient-healthcare system,
becomes relevant.

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