11institutetext: Comilla University, Cumilla, Bangladesh
11email<EMAIL_ADDRESS>22institutetext: Queensland University of
Technology, Brisbane, Australia
22email<EMAIL_ADDRESS>
# Automatic Monitoring Social Dynamics During Big Incidences: A Case Study of
COVID-19 in Bangladesh

Fahim Shahriar 11 Md Abul Bashar 22

###### Abstract

Newspapers are trustworthy media where people get the most reliable and
credible information compared with other sources. On the other hand, social
media often spread rumors and misleading news to get more traffic and
attention. Careful characterization, evaluation, and interpretation of
newspaper data can provide insight into intrigue and passionate social issues
to monitor any big social incidence. This study analyzed a large set of
spatio-temporal Bangladeshi newspaper data related to the COVID-19 pandemic.
The methodology included volume analysis, topic analysis, automated
classification, and sentiment analysis of news articles to get insight into
the COVID-19 pandemic in different sectors and regions in Bangladesh over a
period of time. This analysis will help the government and other organizations
to figure out the challenges that have arisen in society due to this pandemic,
what steps should be taken immediately and in the post-pandemic period, how
the government and its allies can come together to address the crisis in the
future, keeping these problems in mind.

###### Keywords:

Topic Analysis LDA Topic Model Dynamic Topic Modeling Time Series
Decomposition Bengali Text Dataset Newspaper Text Classification RNN LSTM
Sentiment Analysis CNN-BiLSTM

##  1 Introduction

The outbreak of COVID-19 has brought serious health and economic consequences
to society. It triggered one of the largest recessions in the world. Travel
and currency companies lost billions of dollars, global stock markets
plummeted, schools were closed, and the health care system was exhausted.
Mental and social problems arose as people started to worry about infection,
losing friends and family, losing their jobs, or isolation.

Bangladesh has not been rid of this terrible virus. The virus has had major
impacts on people’s lives and significantly degraded quality of life. There
were significant cases of infections and deaths. The hospital did not have
adequate treatment facilities, including doctors, beds, and emergency
supplies. Besides the health crisis, people have suffered enormous economic
losses. Many people have lost their jobs; companies lost revenues, many of
them go bankrupt. The most affected were the day-laborer and low-income
workers. The lockdown in the pandemic suppressed their income. Many workers
starved since their livelihood was cut-off. Working people took to the streets
in search of their livelihood. They started protesting in the streets for
relief. Seeing their plight, many people, including the government, came
forward to help them. Because of the lockdown, the international transport
system was shut down, and stopped imports and exports. As a result, the
country’s industry suffered miserably. Objective monitoring and analysis of
social dynamics during such a big incident can help the government and other
authorities decide and take initiatives where required. This research proposes
utilizing articles published in newspapers to objectively monitor and analyze
social dynamics during a big incidence, such as the COVID-19 pandemic.

Newspapers are one of the most popular mass media in our daily life.
Newspapers provide information on all the country’s financial, political,
social, environmental, etc. Whether it is a public campaign, an emergency, or
a provocation, newspapers are a great resource for keeping track of internal
and external events and stories. This mass media generally provide authentic
information, whereas social media such as Facebook and Twitter often spread
rumors and cannot be relied upon for authentic news. Effective classification,
analysis, and interpretation of newspaper data can provide a deep
understanding of any big incident in a society.

In this research, we analyzed a large spatio-temporal dataset of Bangladeshi
Daily Newspapers related to COVID-19. The approach incorporated volume
analysis, topic analysis, automatic classification of news articles, and
sentiment analysis to better understand the COVID-19 pandemic in Bangladesh’s
divisions and districts over time. The experimental results and analysis will
give an objective insight into the COVID-19 pandemic in Bangladesh that will
benefit the government and other authorities for disseminating resources. This
paper especially shows how to utilize automatic techniques for monitoring
social dynamics in big incidents such as a pandemic, natural disaster, and
social unrest.

This research makes the following main contributions. (1) It collects,
manually classifies, and publishes a large collection of COVID-19 related
Bangladeshi news articles in Bengali and English. (2) It investigates the
topics discussed during the COVID-19 pandemic in Bangladesh and how they have
changed over time using manual and automatic techniques. (3) It designs a CNN-
BiLSTM architecture for analyzing sentiment in Bengali text. (4) It analyzes
COVID-19 related sentiments in the community over time and space. (5) It
automatically categorizes documents into classes of observation interest for
monitoring social interests.

The rest of the paper is organized as follows: Section 2 discusses related
work, Sections 3 discussed methodology and data collection, Section 4 presents
experimental results, and Section 5 concludes the paper.

##  2 Related Work

In this segment, we will discuss some past related works done by different
analysts. We will divide it into four sections: Static Topic Modeling, Dynamic
Topic Modeling, Sentiment Analysis, and Text Classification.

###  2.1 Static Topic Modeling

Topic modeling is a process of discovering hidden topics in a collection of
texts Bashar et al. (2020a); Balasubramaniam et al. (2020). It can be
considered as a factual show of topics through text mining. One of the most
popular topics modeling technique Latent Dirichlet Allocation (LDA) (Blei et
al., 2003; Bashar et al., 2020a) discovers topics based on word recurrence in
a set of documents. LDA is incredibly valuable for finding a sensibly precise
blend of topics inside a given record.

Topic modeling has been well studied for English text mining. For instance,
Zhao et al. (2011) used unsupervised topic modeling in their research and
compared the content of Twitter with the traditional news media _“New York
Times”_. They used the Twitter-LDA model to find topics from a representative
sample of the entire Twitter and then used text mining techniques to compare
these Twitter topics with _New York Times_ ’ topics, taking into account the
topic category and type. Wang and Blei (2011) developed an algorithm to
recommend scientific articles to users in online communities. Their method
combines the advantages of traditional collaborative filtering and
probabilistic topic modeling. They applied collaborative topic modeling for
recommending scientific articles. Wayasti et al. (2018) applied the Latent
Dirichlet Allocation function in the research and extracted topics based on
ride-hailing customers’ posts on Twitter. In their research, they used 40
parameter combinations of LDA to obtain the best combination of topics.
According to the perplexity value, the customers discussed 9 topics in the
post, including keywords for each topic. Tong and Zhang (2016) recommended two
experiments to build topic models on Wikipedia articles and Twitter users’
tweets.

However, topic modeling has not been well studied for Bengali text mining,
unlike English text mining. Das and Bandyopadhyay (2010b) used topic wise
opinion summarization from Bengali text. They applied K-Means clustering and
_document-level theme relational graph_ representation. However, they did not
use any topic modeling technique, such as LDA. Rakshit et al. (2015) applied a
Multi-class SVM classifier for analyzing Bengali poetry and poet relations.
They performed a subject-wise classification of poems into foreordained
categories. Hasan et al. (2019) compared the performance of the LDA and
LDA2vec topic model in Bengali Newspaper. Al Helal and Mouhoub (2018) used LDA
for detecting the primary topics from a Bengali news corpus. However, they did
not directly apply LDA in the Bengali text. Instead, they translated the
Bengali text into English and then applied LDA to detect the topics. Rahman et
al. (2019) used lexical analysis for sentence wise topic modeling. Their topic
modeling was based on sentiment analysis. None of the existing works used
Bengali text topic modeling for monitoring a pandemic or a major event.

In addition to English and Bengali, topic modeling in various languages is
also studied. De Santis et al. (2020) analyzed a system that uses NLP
pipelines, a theoretical framework for content aging to determine the
qualitative parameters of tweets, and co-occurrence analysis to build topic
maps chart splits to identify topics related to posts from Italian Twitter
users. Han et al. (2020) extracted topics related to COVID-19 from Sina
Weibo(Chinese microblogging website) text dataset through the LDA topic model.

###  2.2 Dynamic Topic Modeling

The dynamic topic model is a cumulative model that can be used to analyze
changes in document collection over time Bashar et al. (2020a). There are many
studies on dynamic topic modeling for the English language. For example,
AlSumait et al. (2008) showed that the LDA model could be extended to the
online version by gradually updating the current model with new data, and the
model has the ability to capture the dynamic changes of the topics. Dieng et
al. (2019) researched D-ETM on three data sets and discovered the word
probabilities of eight different topics that D-ETM learned over time. Nguyen
et al. (2020) discovered latent topics from the financial reports of listed
companies in the United States and studied the evolution of the themes
discovered through dynamic topic modeling methods. Marjanen et al. (2020)
discussed humanistic interpretation’s role in analyzing discourse dynamics
through historical newspapers’ topic models. Bashar et al. (2020a) extracted
five COVID-19 related topics from the Twitter dataset through LDA topic
modeling, and they showed the changes in the extracted topics over time.
Nevertheless, for the Bengali language, so far, there is no research on
dynamic topic modeling. In this study, we study the evolution of the extracted
COVID-19 related topics over time using dynamic topic modeling.

###  2.3 Text Classification

Text classification, moreover known as text labeling or text categorization,
is categorizing content into organized bunches Bashar et al. (2020b); Bashar
and Nayak (2020); Bashar et al. (2018); Bashar and Nayak (2020). By utilizing
NLP, classifiers can naturally label text and, after that, relegate a set of
predefined labels or categories based on its substance.

Many researchers worked on text classification in English. For example, Patil
and Pawar (2012) used the Naive Bayes algorithm to classify website content.
They divided the website content into ten categories, and the average accuracy
of the ten categories was almost 80%. Bijalwan et al. (2014) used K-Nearest
Neighbors, Naive Bayes, and Term-gram to classify text. They showed that in
their research, K-Nearest Neighbors’ accuracy was better than Naive Bayes and
Term-gram. Tam et al. (2002) showed that K-Nearest Neighbors was superior to
NNet and Naive Bayes for English documents. Pawar and Gawande (2012) showed
that Support Vector Machines’ performance is far superior to Decision Trees,
Naive Bayes, K-Nearest Neighbors, Rocchio’s algorithms, and Backpropagation
networks. Liu et al. (2010) showed that Support Vector Machines is better than
K-Nearest Neighbors and Naive Bayes.

In addition to English text classification, some researchers have also
classified Bengali text. For example, Mandal and Sen (2014) applied four
supervised learning methods: (Naive Bayes, k nearest neighbor, Decision Tree
classifier, and Support Vector Machine) for labeled web documents. They
classified the documents into five categories: (Business, Sports, Health,
Technology, Education). Chy et al. (2014) applied a Naive Bayes classifier to
categorized Bengali news. Pal et al. (2015) described Naive Bayes classifier
for Bengali sentence classification. They used over 1747 sentences in their
experiment and got an accuracy of 84%. Kabir et al. (2015) used Stochastic
Gradient Descent (SGD) classifier to categorize Bengali documents. Eshan and
Hasan (2017) created an application that identifies abusive texts in Bengali.
They applied Naive Bayes, Random Forest, Support Vector Machine (SVM) with
Radial Basis Function (RBF), Linear, Polynomial, and Sigmoid kernel to
classify the texts and compare the results among them. Islam et al. (2017)
applied SVM, Naive Bayes, and Stochastic Gradient Descent(SGD) to classify
Bengali documents and compare results of those classifiers. However, non of
the existing works used Bengali text classification for monitoring a pandemic
or a major event.

###  2.4 Sentiment Analysis

Sentiment Analysis refers to computationally recognizing and categorizing
opinions communicated in a chunk of text. It is successfully used in commerce
where they use it to track online discussions to identify social estimation of
their brand, item, or benefit.

A lot of research work has been done in sentiment analysis for the English
language. For example, Cui et al. (2006) have reviewed about 100,000 product
reviews from various websites. They divided reviews into two main categories:
positive and negative. Jagtap and Dhotre (2014) applied the Support Vector
Machine and Hidden Markov Model, and the Hybrid classification model is well
suited for extracting teacher feedback and evaluating sentiments. Alm et al.
(2005) divided the seven emotional words into three polarity categories:
positive emotion, negative emotion, and neutral, and the Winnow parameter
adjustment method used can reach 63% accuracy. For extracting the Twitter
sentiment, Agarwal et al. (2011) applied unigram, tree model, and feature-
based model. Bashar et al. (2020a) used Convolutional Neural Network to
extract sentiments related to COVID-19 from the Twitter dataset.

Some research used sentiment analysis in Bengali texts. For instance, Das and
Bandyopadhyay (2010a) classified emotions into six categories: Happy, Sad,
Anger, Disgust, Fear, and Surprise. Chowdhury and Chowdhury (2014) used
sentiment analysis in Bangla Microblog Posts. They applied a semi-supervised
bootstrapping method utilizing SVM and Maximum Entropy. Hasan et al. (2014)
proposed a strategy to identify sentiments in Bengali texts by Contextual
Valency Analysis. They employed the methodology of POS Tagger in their
approach. Hassan et al. (2016) used recurrent neural networks to Romanize
Bengali texts and analyze sentiments. In their experiments, they used Bangla
and Romanized Bangla Text (BRBT) dataset. For Sentiment Analysis of Bangla
Microblogs, Asimuzzaman et al. (2017) used Adaptive Neuro-Fuzzy Deduction
Framework to anticipate extremity and utilized fluffy rules of speech in
semantic rules. Mahtab et al. (2018) designed a model for sentiment analysis
on Bangladesh Cricket news. They applied TF-IDF and SVM (Support Vector
Machine) in their model and found 64.596% accuracy. Tripto and Ali (2018) used
sentiment analysis on Youtube comments. Their research built a model based on
deep learning that classifies a Bengali text into three classes and five
sentiment classes. Tabassum and Khan (2019) used the Random Forest Algorithm
to classify the sentiments in Bengali texts. Tuhin et al. (2019) applied Naive
Bayes and a topic modeling approach to design an Automated System of Sentiment
Analysis in Bengali Text. Their system classifies emotions into six
categories: happy, sad, tender, excited, angry, and scared. However, non of
the existing works used Bengali text sentiment analysis for monitoring a
pandemic or a major event.

##  3 Experimental Methodology

###  3.1 Methodology

This pandemic situation has changed society and the country by a significant
margin. The whole face of the country has changed completely. Some significant
sectors of the nation, such as economic, social, political, have been affected
massively. The education systems have been hit particularly hard. This
research aims to automatically analyze the daily newspapers in Bangladesh to
reveal what is going on in society and gain knowledge to comprehend the
fundamental topics (or subjects) and sentiment arising and advance in the
discussion.

This study will conduct a topic and sentiment analysis on a large collection
of COVID-19 related news articles published in Bangladesh both in Bengali and
English texts. The study will focus the analysis on both spatial and temporal
dimensions. In the topic analysis, we used LDA-based topic modeling and
dynamic topic modeling to find the topics, their evolution over time, and
their time and space (location). We also analyzed what impact each topic had
on particular areas. Then we analyzed the sentiment distribution over time and
space to identify social sentiment in space and time. The experimental
workflow of this study is shown in Figure 1.

First, we manually gathered a large collection of COVID-19 related news
articles from Bangladeshi six most circulated daily newspapers. Along with the
news, the collection contains geospatial and temporal information on the news.
The dataset was then preprocessed by removing HTML, markers, and other non-
relevant information such as adverts.

Then, we manually organized the news articles in a set of classes and sub-
classes. Then we extracted the topics and the subtopics from the dataset. We
used these classes and sub-classes to perform basic analysis such as comparing
similarity and diversity in the news. These classes and sub-classes have also
been used to qualitatively evaluate the accuracy of the topics discovered by
LDA and labels predicted by classifiers before LDA and classifiers are
employed for detailed analysis.

Figure 1: Experimental Work flow

###  3.2 Data Collection and Preparation

These publicly available News articles related to COVID-19 have been collected
from the six most popular newspapers in Bangladesh from 21 January 2020 to 19
May 2020. The six newspapers are _The Daily Prothom Alo_ , _Bangladesh
Pratidin_ , _Kaler Kantho_ , _The Daily Star_ , _Newage_ , and _The Daily
Observer_. A total of 15,565 news articles are collected from these six
newspapers. From every news article, we extracted the news title, the main
body of the news, a summary of the news (i.e., first few lines of the news
body), the published date, and the news incident’s location. We used Python’s
_BeautifulSoup_ and _Newspaper3k_ tool for extracting the news content.
_BeautifulSoup_ is a popular Python package for parsing HTML and XML archives
and one of the most popular web scraping tools. _Newspaper3k_ is a user-
friendly library for scraping the news articles and other related data from
newspaper portals. It is built upon request and used to parse LXML. This
module is an improved version of the _Newspaper_ module and is also used for
the same purpose. Table 1 summarizes the statistics of the collection. We call
this collection _Comilla University COVID-19 News Collection_ (CoU-CNC). We
made it available online111CoU-CNC Dataset: https://cutt.ly/djGILi2 for anyone
for further analysis.

Article Source | Language | Article Count
---|---|---
The Daily Prothom Alo | Bengali | 4169
Bangladesh Pratidin | Bengali | 5584
Kaler Kantho | Bengali | 1160
The Daily Star | English | 1278
The Daily Observer | English | 1191
New Age | English | 2183
Table 1: CoU-CNC Dataset Statistics

Out of these six newspapers, news articles in three newspapers (_The Daily
Prothom Alo_ , _Bangladesh Pratidin_ , _Kaler Kantho_) are composed in the
Bengali language, and in the other three newspapers (_The Daily Star_ , _The
Daily Observer_ , _New Age_) articles are composed in the English language.
There are 10,913 news articles in the Bengali language, and the remaining 4652
news articles are in the English language.

As the dataset has 4,652 articles in English and we wanted all articles in the
same language to be better parsed, so we translated the English articles into
Bengali via Python’s _googletrans_ module. As a result, after translating
these articles, all the articles are in the Bengali language. Then, we applied
tokenization to split a string of text into smaller tokens. The news articles
are split into sentences, and sentences are tokenized into words. Then, we
applied noise removal (e.g., removing HTML tags, extra white spaces, special
characters, numbers) to clean up the text. Then, we removed the stopwords from
the document. As there is no build-in stopwords module for Bengali nltk, we
manually created a stopword list and made it available online222Bengali-
Stopwords: https://cutt.ly/2jXbDRB. Then, we expanded contraction. We set the
minimum letter length to 6. We also removed all the words that were below the
minimum letter length. There are no good resources for stemming and
lemmatization in the Bengali language. So, we applied stemming and
lemmatization to the tokens in our own process. After removing all the
stopwords and other noises, there were a total of 80,693 tokens. There are
some specific suffixes for the Bengali language. The suffix removal from the
word has also been done with the help of Python. We used
_Bangla_Steamer.Steamer_ library of Python to improve accuracy. However, it
did not show the expected results as the library is effective for a small
number of Bengali words. To increase the accuracy of this 80,693 sizes
lemmatized dictionary, we manually verified about 30000 most frequent tokens
from 80693 words. We lemmatized where we needed to lemmatized manually, and we
also corrected the incorrect and misspelled words where it was needed. Many
more words are manually lemmatized and corrected through this manually 30,000
words check. We have published verified Bengali words on the Internet and
titled _“Modified Bengali Words”_ 333Modified Bengali Words:
https://cutt.ly/8jE6GIC for further analysis.

To compare the number of news published and the COVID-19 cases of Bangladesh,
we collected an open-source
dataset444https://www.worldometers.info/coronavirus/country/bangladesh/ of
confirmed COVID-19 cases and death cases of Bangladesh from March 8 to May
19.We also collected another open-source
dataset555https://data.humdata.org/dataset/district-wise-quarantine-for-
covid-19 of confirmed cases based on divisions and districts of Bangladesh
from March 8 to May 19.

####  3.2.1 Class Distribution in News Articles

After collecting the new articles, first, we analyzed them manually. In this
process, we extracted eight classes (shown in Table 2) and 19 sub-classes from
the news articles. The representation of the extracted eight classes and the
hierarchical organization of sub-classes are shown in Figure 2. The
distribution of the extracted classes over news articles is shown in Figure 3
and the distribution of the extracted sub-classes over news articles is shown
in Figure 4.

Table 2: Eight Classes Extracted from the Collected News Articles (1)
Statistics, (2) Social Information, (3) COVID-19 Effects, (4) COVID-19
Responses and Preventive Measure, (5) Government Announcement and Responses,
(6) Solidarity and Cooperation, (7) International Information, and (8) Health
Organization Responses
---
Figure 2: Manually Extracted Classes and Sub-classes Figure 3: Distribution of
Manually Extracted Classes over News Articles Figure 4: Distribution of
Manually Extracted Sub-classes over News Articles

###  3.3 Spatio-Temporal Analysis

Time Series or temporal analysis of newspaper articles is utilized to observe
the transient expansion during the pandemic. Time series decomposition
includes considering a series of components in the time dimension: Level,
Trend, Seasonality, and Noise segments. Level refers to the average value in
the series, Trend refers to the increasing or decreasing value in the series,
Seasonality refers to the repeating short-term cycle in the series, and
finally, Noise refers to the random variation in the series. Decomposition
gives a powerful supportive model for pondering time series and better
arrangement issues during time series analysis and decision making. The
additive model (Dagum, 2010) suggests that the segments are added as the
following formula:

$y(x)=l(x)+t(x)+s(x)+n(x)$ (1)

where $y(x)$ represents the additive model, $l(x)$ represents the observed
level, $t(x)$ represents the trend, $s(x)$ represents the seasonality and
$n(x)$ represents the noise or residual in the signal $x$. This model is
linear. The change over a period of time is reliably affected by the similar
sum of the linear trend as a straight line. A linear seasonality has a similar
recurrence and abundance. On the other hand, A multiplicative model (Dagum,
2010) recommends that the components are multiplied together as the following
formula:

$y(x)=l(x)\times t(x)\times s(x)\times n(x)$ (2)

where $y(x)$ represents the multiplicative model, $l(x)$ represents the
observed level, $t(x)$ represents the trend, $s(x)$ represents the seasonality
and $n(x)$ represents the noise in the signal $x$. A multiplicative model is
exponential or quadratic when expanding or diminishing over the long run. A
nonlinear pattern is a bent line. In this examination, we disintegrated the
time series utilizing the multiplicative model. For Spatial Analysis, we used
Tableau Software to compare the number of news published, and the number of
COVID-19 confirmed cases geographically.

###  3.4 Static Topic Analysis

Analyzing the topics of news articles published during a major incident or a
pandemic like COVID-19 can help monitor the situation and understand the
public concerns, which is critical for government authorities and charity
organizations to disseminate required resources and aids. However, in such a
situation, a large number of news articles are published in various
newspapers. We observed that as the situation deteriorated during the
pandemic, newspapers had to publish much news on various topics. Manually
analyzing the topics by reading a large number of articles is time-consuming
and expensive. We utilized two unsupervised machine learning techniques: (a)
LDA (Blei et al., 2003), a popular topic modeling technique, as static topic
modeling to automatically find topics of articles published in newspapers, and
(b) dynamic topic modeling in (Blei and Lafferty, 2006) to see how those
topics evolve over the long haul.

LDA is a Bayesian probabilistic model that discovers topics and provides topic
distribution over documents and word distribution over topics. It has two
phases: (a) the first phase models each document as a composition of topics,
and (b) the second phase models each topic as a composition of words. LDA
utilizes word co-occurrences inside documents for discovering topics in a
document assortment. Words occurring in an equivalent document are practically
coming from the same topics, and documents containing comparative words will
undoubtedly include comparable topics. In this research, the _Gensim_ package
in Python was utilized to execute the LDA model. We utilized every news
article as a document in the topic modeling. Before applying the LDA topic
model, we manually associated documents into general classes and sub-classes
to know about the quality of LDA extracted fine-grained topics.

Then, we analyzed each LDA extracted topic’s temporal trends to see when a
topic has been discussed more or published more in the newspapers. Finally, we
analyzed each topic’s spatial distribution to see what effect each had in a
particular place. We used Tableau software to analyze the spatial distribution
of each topic.

###  3.5 Dynamic Topic Analysis

The static topic modeling treats words as interchangeable and indeed treats
documents as interchangeable. However, the presumption of replaceable
documents is impractical for some assortments when accumulating along the
time. For example, tweets, news articles, and insightful articles as they are
advancing substance along time. The subjects in a newspaper article assortment
develop, and it is essential to display the elements of the fundamental topics
unequivocally.

Dynamic topic modeling extends the static theme, which illustrates the
progression of the theme in consolidate. Dynamic topic modeling can catch the
development of topics in a successively coordinated assortment of news
articles. In this research, the articles are synchronized by week. We used the
dynamic topic model to analyze discussion topics and topic changes over time.

###  3.6 Text Classification

Then we built a text classifier to verify their performance and predict the
class, sub-class, and topics in the unknown (upcoming in the future) news
articles. Such classification is important when we need to monitor a specific
category (or class or group) of news. We made Long Short-Term Memory (LSTM)
Recurrent Neural Network (RNN) models in Python utilizing Keras deep learning
library for text classification. RNN is a special kind of neural network where
the previous step’s output will be used as the current step’s input. In a
traditional neural network, not all inputs and outputs are interdependent.
However, interdependence is an important part of text data. In such cases, the
model needs to predict the next word given the previous words, so the previous
word must be stored. Thus RNN was born, which solved the problem with the help
of hidden layers. The primary function of RNN is also essential, namely the
_hidden state_. It can remember some information about the sequence.

RNN is a neural feedback network that operates on the internal memory. Since
the RNN has a similar function for each piece of information and the current
range’s output is based on the last count, the RNN is essentially recursive.
When there is an output, it is copied and sent back to the relay network. The
current input and the output of the previous input are taken into account in
determining the prediction of the next word. Unlike direct feedback neural
networks, RNNs can use their internal state (memory) to manage the input
elements’ interdependence. That makes them useful for text data, handwriting
recognition, or speech recognition. The architecture of an unrolled recurrent
neural network is shown in Figure 5.

Figure 5: Unrolled Recurrent Neural Network

In Figure 5, first the model gets $x_{0}$ from the input sequence. Then it
produces $h_{0}$, which is used in the next input to the model along with
$x_{1}$. That is, both $h_{0}$ and $x_{1}$ become inputs to the next step.
Then, $h_{1}$ and $x_{2}$ are input to the next step, and so on. Like this,
RNN continues summarizing the unique circumstance in the hidden state while
training. Then, it uses the summarized hidden state to classify the sequence
(Bashar et al., 2020b).

###  3.7 Sentiment Detection

We proposed a hybrid neural network model based on Convolutional Neural
Network (CNN) and LSTM for sentiment analysis in Bengali texts.

Integrated models are used to solve various vision and NLP problems and
improve a single model’s performance. The following subsections provide an
overview of the LSTM and CNN models offered. In subsection 3.6, we described
LSTM. In this research, we used two-layer bi-LSTM, word embedding include
words in the news articles and provide sentiments.

Another part of our proposed structure is based on Convolutional Neural
Network (CNN). CNN has very successful in various image processing and NLP
tasks these last years. They are powerful in exploration, achieving local
relevance, and data standards through learning. Generally, to rank text on
CNN, different words in sentences (or paragraphs) need to be placed. Stacked
to form a two-dimensional matrix, pleated filters (different lengths) are
applied to the window. To use CNN for text classification, the different words
stacked in a sentence are usually stacked in a two-dimensional matrix, and
afterward, a convolution is applied to the word in the window in one word to
be created applied a new function declaration. Then, a max-pooling is applied
to the new function, and the combined functions of different filters are
combined to shape a concealed portrayal. Completely associated layers trail
these portrayals for the last estimate. The architecture of our CNN-BiLSTM
Hybrid network model is shown in Figure 6.

Figure 6: The architecture of CNN-BiLSTM Hybrid network for Sentiment
Identification

We created a sequential model that includes an LSTM layer. Then we made our
model sequential and adding layers. In the first layer, we applied a conv1D
with 200 as a filter for CNN. After that, we applied two Bi-LSTMs on the
second and third layer with an error of 0.5. Then we applied a dense network
on the remaining levels. We also used _Adam_ as an optimizer with tight
hyperparameters and applied _L2_ adjustments to reduce overfitting as much as
possible. We only used five epochs, as using more epochs resulted in
overfitting and kept the stack size of 256, as it worked very well.

##  4 Experimental Results

###  4.1 Volume Analysis

####  4.1.1 Temporal Analysis of Volume

The time series volume analysis of newspapers is shown in Figure 7. The figure
has four plots, namely observed level, trend, seasonal, and noise or residual.
The first plot Figure 7a shows the original volume, i.e., the number of
COVID-19 related news articles in a time point. It shows that the curve began
to rise from January when some COVID-19 cases were found in China and other
countries. The plot increased sharply in early March when a few instances of
COVID-19 cases were identified in Bangladesh. The curve remained high onward
with some fluctuations. The second plot Figure 7b shows the trend of the
COVID-19 related news publication volume. It shows that the COVID-19 related
news started becoming trendy by the end of January, and the trend increased
significantly in early March. The trend stayed high through the rest of the
time with some fluctuations. The third plot Figure 7c shows the seasonal,
cyclical change in the volume. Moreover, the fourth plot Figure 7d shows a
residual or random variation in the volume.

(a) Observed

(b) Trend

(c) Seasonal

(d) Residual

Figure 7: Time Series Decomposition

To see how newspapers reacted during the COVID-19 pandemic, we tracked
COVID-19 cases, death from COVID-19, and COVID-19 news volume in Figure 8. The
figure shows that the newspapers were vigilant from the beginning of the
pandemic. The newspaper journalists increased COVID-19 related news coverage
exponentially as soon as COVID-19 cases were found in Bangladesh in early
March. The news volume continued increasing until the last quarter of March.
This part of the news volume shows the newspapers reacted from about COVID-19
from the very early pandemic stage. They significantly covered the pandemic
during the early period of the COVID-19 cases.

The number of identified cases increased significantly by the second quarter
of April, and it continued to increase. However, the number of COVID-19
related news articles did not increase during this time. Even in some cases,
the news article volume decreases marginally. The possible reasons might be:
(a) Because Bangladesh is a developing country, to survive at this point,
people had to think more about earnings than pandemic. As a result, pandemic
news did not increase attention, and newspapers did not increase COVID-19
related articles. (b) Some other big incidences gained more attention than
COVID-19. (c) The newspapers reached their allocated space for pandemic news
already.

Figure 8: Comparison of Daily News Article Counts and Daily Cases (21 January
2020 - 19 May 2020)

####  4.1.2 Spatial Analysis of Volume

The spatial Distribution of Bengali newspapers is shown in Figure 9a. The
number of news articles was concentrated on the central part of Bangladesh,
mainly Dhaka, Narayanganj, and Gazipur. More than 6000 COVID-19 related news
articles were published in Bangladeshi newspapers related to Bangladesh’s
central part. More than 2000 news articles related to the southern part of
Bangladesh, mainly Chittagong and Cox’s Bazar.

(a) Number of News Published Up to 19 May 2020

(b) Confirmed Cases Up to 19 May 2020

Figure 9: Spatial Analysis of News Article Volume

The spatial distribution of confirmed cases of COVID-19 is also shown in
Figure 9b. The central part of Bangladesh is the most affected area. More than
10,000 COVID-19 patients were identified in Dhaka during this time. Outbreaks
have been reported in the surrounding areas of Dhaka, mainly Narayanganj and
Gazipur. After the Dhaka division, we can see the highest infection rate in
the southern part of Bangladesh, mainly Chittagong. Figure 9 shows a
correlation between the number of confirmed COVID-19 cases in an area and the
published news volume related to that area. This means automatic monitoring of
news article volume can give a clear view of the severity of a pandemic or big
instances in a society.

Figure 10: District-wise Distribution of News Articles Figure 11: Division-
wise Distribution of News Articles

Figure 12: Geo-spatial and Temporal Distributions of News Articles Published
over Time. Horizontal axis shows consecutive weeks in the duration and
vertical axis shows the volume (count of news articles).

The district-wise break down of published news articles for significant volume
is shown in Figure 10 and division-wise break down in Figure 11. The figures
show that most news published was related to the Dhaka district and Dhaka
division. More than 57% of the published news was related to the Dhaka
division. After Dhaka, most news has been published on Chittagong. More than
19% of the news was related to the Chittagong division. The geospatial and
temporal distributions of newspaper articles are shown in Figure 12. The
figure shows that the volume of published news articles related to each
location significantly changed over time, lower volume before and beginning of
the pandemic, and significantly increased during the pandemic.

###  4.2 Topic Analysis

####  4.2.1 Topic Extraction

For topic analysis through the LDA topic model, it is indispensable to decide
the optimal number of topics. Seeking an appropriate LDA topic number and
clarifications to examine the relationship between the COVID-19 emergency and
news articles, we have given much thought. We used a coherence score and
perplexity score to assess the choice of an appropriate number of topics.
After preprocessing the data, we applied the LDA model to discover hidden
topics in news articles. To determine the optimal number of topics, we
diagnosed the coherent score and the perplexity score graph shown in Figure
13. Figure 13a is showing the coherent score graph and Figure 13b is showing
the perplexity score graph.

(a) Coherent Score (b) Perplexity Score

Figure 13: Determining optimal number of topic

From the coherence score graph, we got the highest coherence score (0.5077)
when we set the number of topics to 9, shown in Figure 13a. Moreover, from the
perplexity score graph, we got the highest perplexity score (-7.59) when we
set the number of topics to 24, shown in Figure 13b. We chose the coherent
score between the coherent score and perplexity score as the optimal number of
topics for the coherent score is 9, which is very close to the number of
manually extracted classes of 8, shown in Table 2. So we set the number of
topics for LDA topic extraction to 9. The word clouds for top words (i.e.,
keywords) in each of the nine topics is shown in Figure 14. The weights and
appearance counts of the keywords in each topic is shown in Figure 15. The
visualization of the clusters of documents in a 2D space using the t-SNE
(t-distributed stochastic neighbor embedding) algorithm is shown in Figure 16.
In Figure 17, inter-topic distance map and 30 relevant keywords are displayed
for each topic. They discovered nine topics are listed in Table 3.

Table 3: Nine Topics Discovered by LDA (1) Economic Crisis and Incentives, (2)
Epidemic Situation and Outbreak, (3) Vaccine and Treatment, (4) Demonstration
for Wages and Relief, (5) Medical Care and Health Organization Responses, (6)
Repatriation and International Situations, (7) Daily Infected Death and
Recovered Cases, (8) Strategic Preparedness, and (9) Government Announcement
and Responses
---

Figure 18 shows the topic frequency ratio in the document collection (news
articles). The figure shows that Topic 8 (Strategic Preparedness) is the most
frequent topic amongst all the nine topics discovered by LDA, and this topic
accounted for 26.3% of all the nine topics. The second most frequent LDA topic
is Topic 2 (Epidemic Situation and Outbreak), which accounted for 20.1%. Topic
9 (Government Announcement and Responses) and Topic 7 (Daily Infected, Death,
and Recovered Cases) are 13.6% and 11.7%, respectively, and are the third and
fourth most frequent topics. Topic 5 (Medical Care and Health Organization
Responses), Topic 3 (Vaccine and Treatment), and Topic 4 (Demonstration for
Wages) and Relief are at fifth, sixth, and seventh positions, and They
accounted for 9.8%, 5.7%, and 5.2%, respectively. Finally, Topic 6
(Repatriation and International Situations) and Topic 1 (Economic Crisis and
Incentives) are the least frequent topics, and the proportion of these two
topics is less than 5%. By reviewing all these topics and analysis, we can
insight into the pandemic or any important incident in a society.

(a) Word cloud of Topic 1

(b) Word cloud of Topic 2

(c) Word cloud of Topic 3

(d) Word cloud of Topic 4

(e) Word cloud of Topic 5

(f) Word cloud of Topic 6

(g) Word cloud of Topic 7

(h) Word cloud of Topic 8

(i) Word cloud of Topic 9

Figure 14: Word Clouds for nine topics

(a) Word counts of Topic 1

(b) Word counts of Topic 2

(c) Word counts of Topic 3

(d) Word counts of Topic 4

(e) Word counts of Topic 5

(f) Word counts of Topic 6

(g) Word counts of Topic 7

(h) Word counts of Topic 8

(i) Word counts of Topic 9

Figure 15: Word Counts for nine topics

Figure 16: t-SNE clustering chart Figure 17: Interactive Topic Visualization
Figure 18: Proportion of Topic Frequency Distribution in the News Collection

(a) Topic 1 (b) Topic 2 (c) Topic 3 (d) Topic 4 (e) Topic 5 (f) Topic 6 (g)
Topic 7 (h) Topic 8 (i) Topic 9

Figure 19: Topics Evolution of nine topics

####  4.2.2 Dynamic Topic Modeling: Temporal Trends of Topics

To show the evolution of topics overtime during the pandemic, we used Dynamic
Topic Modeling Blei and Lafferty (2006). Figure 19 shows the evolution of nine
topics over weeks during the pandemic. The figure shows how the popularity of
each topic and the top words (i.e., keywords) in the topic changed over time.
The overall temporal trend of these topics is shown in Figure 20.

Topic 1: “Economic Crisis and Incentives” climbed from early March and stopped
rising at the end of March. Then the curve continued to decline until the
beginning of April and rose slowly in the middle of April. Then again, the
curve continued to decline until the beginning of May. Then finally it reached
a peak in the middle of May.

Topic 2: “Epidemic Situation and Outbreak” climbed from the beginning of March
and stopped rising at the end of April. The curve then steadily continued to
decline until the beginning of April. Then slowly rose the curve to the middle
of April. Furthermore, again continued to decrease the curve until the
beginning of May and then rose to a peak in the middle of May finally.

Topic 3: “Vaccine and Treatment” climbed from the end of February and stopped
growing at the beginning of March. Then the curve steadily continued to
decline until the middle of March. And then again started to rise and reached
a peak at the end of March. Then it also started to decline the curve until
the middle of May. After that, the curve fluctuated till the end.

Topic 4: “Demonstration for wages and relief” climbed from the beginning of
March. Then the curve stopped rising in the middle of March. After that, the
curve was fluctuating between the middle of March and the middle of April, and
then the curve reached a peak in the middle of April. Then the curve steadily
declined until the beginning of May and rose slowly in the middle of May.

Topic 5: “Medical Care and Health Organization Responses” climbed from the
beginning of February and stopped rising in early March. Then the curve
started to rise from the beginning of March. In the middle of April, the curve
reached its peak point. Then The slope of the curve gradually deteriorates
after the middle of April.

Topic 6: “Repatriation and International Situations” climbed from the
beginning of February and stopped rising early in March. The curve then
steadily declined and then again started to rise from early March to the
middle of March. After that, the curve had a steady state for a while; it went
to the top at the end of March. Then again, the curve steadily declined until
the beginning of April. Then the curve fluctuated till the end.

Topic 7: “Daily infected, death and recovered cases” climbed from the
beginning of February. As the number of infected and death cases was growing
every day so that the number of daily infected deaths, death cases were also
rising every day. So the curve was also rising. The curve reached its peak at
the end of April. Then the curve declined until the beginning of May. After
that, the curve again started to rise from the end of April to the end.

Topic 8: “Strategic Preparedness” climbed from the beginning of March and
stopped rising at the end of March. Then the curve declined until the end of
March. After that, the curve started to rise from the end of March. Suddenly
the curve downgraded for a while and then again started to rise up and then it
peaked. The curve then steadily declined until the beginning of May and rose
slowly in the middle of May.

Topic 9: “Government Announcement and Responses” climbed from the beginning of
March and reached a peak in the middle of March. Then the curve declined until
the middle of April. After that, the curve again started to fluctuate. Then
also, the curve steadily deteriorated until the end of April. Furthermore,
finally, the curve rose slowly till the end.

(a) Temporal Trends of Topic 1

(b) Temporal Trends of Topic 2

(c) Temporal Trends of Topic 3

(d) Temporal Trends of Topic 4

(e) Temporal Trends of Topic 5

(f) Temporal Trends of Topic 6

(g) Temporal Trends of Topic 7

(h) Temporal Trends of Topic 8

(i) Temporal Trends of Topic 9

Figure 20: Overall Temporal Trends of Topics

####  4.2.3 Spatial Distribution of Topics

This subsection details the experimental results of the spatial distribution
of the topics.

Topic 1: “Economic Crisis and Incentives” mainly concentrated on the Dhaka
division shown in Figure 21a. Most people of the Dhaka division lost their job
in that period. This incident also happened in Chittagong to a small extent.
The government and various agencies provided Relief and incentives to the
victims. In this case, the area that has received the most relief is the Dhaka
and the Chittagong division.

Topic 2: “Epidemic Situation and Outbreak” mainly focused on the central and
southern parts of Bangladesh, shown in Figure 21b. We can see that Dhaka is
the most affected city in Bangladesh at that particular time. Most COVID-19
infected patients have been identified in Dhaka, more deaths have been
reported in Dhaka, the situation in Dhaka was much worse than other districts
and divisions at that time, and there was a much higher prevalence. Apart from
Dhaka, Narayanganj, Gazipur, Chittagong has also been affected so much.

Topic 3: “Vaccine and Treatment” mainly focused on Dhaka, Chittagong, Gazipur,
Narayanganj area in Bangladesh, shown in Figure 21c. Since the prevalence of
COVID-19 is higher in Dhaka, its adjoining districts like Narayanganj,
Gazipur, and Chittagong so that treatments were shouted comparatively higher
than in other districts. COVID-19 vaccine is also being studied in Dhaka.

Topic 4: “Demonstration for wages and relief” is mainly concentrated all over
the country shown in Figure 21d. The situation is terrible all over the
country due to COVID-19. Those who are day laborers have lost their jobs; they
have become destitute. For this, they had to come out of the house to survive.
They had to move on the streets to provide food for their families. Since in
every district of Dhaka, Chittagong, Rajshahi, Barisal, Khulna, Mymensingh,
Sylhet, Rangpur, people had taken to the streets to protest for survival.

Topic 5: “Medical Care and Health Organization Responses” also focused all
over the country like Topic 4, shown in Figure 21e. The state of the health
system in the whole country is deplorable. Health organizations were in a very
critical situation.

Topic 6: “Repatriation and International Situations” mainly concentrated on
China, USA, Italy, Russia, and other countries shown in Figure 21f. This topic
talks about the situations of foreign countries and the immigrants who wanted
to return to Bangladesh. Here those countries have been shown in Figure 21f.

Topic 7: “Daily infected, death and recovered cases” mainly focused on the
central region, shown in Figure 21g. Dhaka division has the highest number of
COVID-19 infected cases, as well as the death cases. Dhaka division includes
Dhaka city, Narayanganj, Gazipur had the most cases. After Dhaka, most of the
cases were found in the Chittagong division.

Topic 8: “Strategic Preparedness” focused on all over the country shown in
Figure 21h. Lockdown, isolation, home quarantine, social distancing was
imposed across the country.

Topic 9: “Government Announcement and Responses” shown in Figure 21i. It was
effective almost everywhere, especially in Dhaka.

(a) Topic 1

(b) Topic 2

(c) Topic 3

(d) Topic 4

(e) Topic 5

(f) Topic 6

(g) Topic 7

(h) Topic 8

(i) Topic 9

Figure 21: Spatial Distribution of Topics

###  4.3 Automatic Classification of News Articles

We built a text classifier to automatically categorize upcoming new news
articles into classes, sub-classes, and topics. We implemented an LSTM
recurrent neural network model in Python utilizing Keras666https://keras.io/
deep learning library for this classification.

We splited our data into 80% for training, 10% for validation, and 10% for
testing. In the data preparation, first, we cleaned the text by removing
unnecessary characters and stopwords. After cleaning the text, we tokenized
the data using Keras Tokenizer. After that, we built a word index from it.
Then we vectorized Bengali text by turning each text into a vector. We limited
the dataset to the top 50,000 words and set the max number of words in each
article at 1000. After that, we added padding and truncated to our data to
make the input sequences uniform and the same length for modeling.

After cleaning the data, we selected pre-trained word embeddings 777Bengali
Word Embeddings: https://cutt.ly/KjXmEio. Word embedding maps each word from
the vocabulary to a vector of real numbers. We used these pre-trained word
embedding in the embedding layer of our LSTM model.

In our classification model, the first layer is the embedded layer that
employments 300 length vectors to represent each word. The second layer is an
LSTM layer with 100 hidden units. The final layer is a dense layer, also known
as the output layer. This final layer has a length of 8, 19, and 9 for the
classes, sub-classes, and LDA-discovered topics, respectively. _Softmax_ is
used as the activation function for multi-class classification in the final
layer. We used _categorical cross-entropy_ as the loss function, _Adam_ as the
optimizer, and a batch size of 32. We used only five epochs as it worked quite
well.

The experimental results for Precision, Accuracy, F1 score and Recall are
given in Table 4. The Precision, Accuracy, F1 score, and Recall are 47.80%,
44.39%, 45.13%, and 42.80%, respectively, for the eight classes. For the 19
sub-classes, Precision, Accuracy, F1 score, and Recall are 47.33%, 38.51%,
37.20%, and 30.82%, respectively. Furthermore, we also computed the
performance of 9 LDA topics. For the 9 LDA topics, Precision, Accuracy, F1
score, and Recall are found 81.37%, 79.55%, 79.67%, and 78.10%, respectively.

Criteria | Classes | Sub-classes | Topics
---|---|---|---
Precision | 47.80% | 47.33% | 81.37%
Accuracy | 44.39% | 38.51% | 79.55%
F1 Score | 45.13% | 37.20% | 79.67%
Recall | 42.80% | 30.82% | 78.10%
Table 4: Performance in Classes, Sub-classes, and Topics

###  4.4 Sentiment Analysis

We analyzed sentiment in the COVID-19 related news articles to see how
positively and negatively the society was affected by the COVID-19 (or any big
incident). We also analyzed the effectiveness of a hybrid CNN-BiLSTM model in
identifying sentiments in Bengali texts. First, we manually labeled each news
article according to positive or negative sentiment in the article. Then we
trained CNN-BiLSTM to detect the sentiment of any upcoming new articles. After
labeling the articles’ positive/negative sensation, we visualized the results
in Figure 22. The figure shows that there were more negative sentiment news
articles than positive sentiments.

Figure 22: Proportion of Positive and Negative Sentiments

Figure 23: Spatial and Temporal Distribution of Number of Positive and
Negative Sentiment News Articles

We prepared our labeled news collection as 80% of for training, 10% for
validation, and 10% for testing for sentiment analysis. Using Keras Tokenizer,
we tokenized the data after cleaning the dataset. Then we built a word index
and vectorize each text. We retrained the dataset to the 60,000 top words and
set the max number of words in each article at 200 using feature selection. We
added padding and truncated the data to make the input sequences uniform and
the same for modeling.

After data preparation, we built our model. The first layer of the model is
the embedding layer. We set the embedding dimension to 300 for embedding each
word. In the second layer, we started a conv1D with 200 filters for CNN. Then
in the third and fourth layers, we applied two Bi-LSTM with a dropout of 0.5.
In the final layer, we used a dense network. We used _Adam_ as the optimizer
with finely tuned hyperparameters and applied _L2_ regularizations to reduce
overfitting. We kept the batch size of 256 as it worked quite well. We used
only five epochs that gave us reasonably good results.

After calculating the Precision, Accuracy, F1 score, and Recall, the
performance and the sentiments of the classification presented in Table 5. The
Precision, Accuray, F1 score, and Recall are 74.89%, 74.94%, 74.88%, and
74.89%, respectively, for sentiment analysis.

Criteria | Performance
---|---
Precision | 74.89%
Accuracy | 74.94%
F1 Score | 74.88%
Recall | 74.89%
Table 5: Performance of Sentiment Analysis

Figure 23 shows the spatial and temporal distribution of the number of
positive and negative sentiment news articles during the pandemic. It shows
how sentiments are changing over eight divisions for 20 weeks.

##  5 Conclusions

This study took an in-depth analysis of Bangladeshi daily newspaper reports
from the onset of the COVID-19 pandemic. After collecting the news articles,
we investigated and manually classified them into eight classes and nineteen
sub-classes. We used LDA for extracting nine COVID-19 related topics from the
news articles. We used the dynamic topic model to see the evaluation of topics
over time. We also provided the spatial distribution of the topics. We created
a text classifier that will automatically sort upcoming articles into classes,
sub-classes, and topics. We also did a spatial and temporal analysis of news
article volume. In the temporal analysis of volume, we decomposed the time
series into four components: observed, trend, seasonal, and residual. Besides,
we analyzed daily news article counts and daily infected and death cases in
the temporal and spatial dimensions. Finally, we analyzed the sentiments in
the news articles related to COVID-19 to understand the positive and negative
impacts of events and initiatives during the COVID-19 pandemic using a CNN-
BiLSTM architecture.

In a period of big social incidence, continuous analysis of newspaper articles
is essential to ensure public well-being, maintain social consensus, and save
lives. The automatic analysis techniques and the analysis outcomes presented
in this study will help government and crisis reaction faculty improve public
comprehension, evaluation, predisposition, quicken emergency reaction, and
backing post-incidence administration.

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