An Ensemble Based Approach for Sentiment Classification in Asian Regional Language

Abstract

In today's digital world, millions of individuals are linked to one another via the Internet and social media. This opens up new avenues for information exchange with others. Sentiment analysis (SA) has gotten a lot of attention during the last decade. We analyse the challenges of Sentiment Analysis (SA) in one of the Asian regional languages known as Marathi in this study by providing a benchmark setup in which we first produced an annotated dataset composed of Marathi text acquired from microblogging websites such as Twitter. We also choose domain experts to manually annotate Marathi microblogging posts with positive, negative, and neutral polarity. In addition, to show the efficient use of the annotated dataset, an ensemble-based model for sentiment analysis was created. In contrast to others machine learning classifier, we achieved better performance in terms of accuracy for ensemble classifier with 10-fold cross-validation (cv), outcomes as 97.77%, f-score is 97.89%.

Full text

An Ensemble Based Approach for Sentiment Classification in Asian Regional
Language
Mahesh B. Shelke
1
, Jeong Gon Lee
2
,
*
, Sovan Samanta
3
, Sachin N. Deshmukh
1
, G. Bhalke Daulappa
4
,
Rahul B. Mannade
5
and Arun Kumar Sivaraman
6
1
Department of Computer Science and Information Technology, Dr. Babasaheb Ambedkar Marathwada University, Aurangabad,
Maharashtra, 431004, India
2
Division of Applied Mathematics, Wonkwang University, 460, Iksan-daero, Iksan-Si, Jeonbuk, 54538, Korea
3
Department of Mathematics, Tamralipta Mahavidyalaya, Tamluk, West Bengal, 721636, India
4
Department of Electronics and Telecommunication Engineering, AISSMSCOE, Pune, Maharashtra, 411001, India
5
Department of Information Technology, Government College of Engineering, Aurangabad, Maharashtra, 431005, India
6
School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, 600127, India
*Corresponding Author: Jeong Gon Lee. Email: jukolee@wku.ac.kr
Received: 30 January 2022; Accepted: 23 March 2022
Abstract: In today’s digital world, millions of individuals are linked to one
another via the Internet and social media. This opens up new avenues for infor-
mation exchange with others. Sentiment analysis (SA) has gotten a lot of attention
during the last decade. We analyse the challenges of Sentiment Analysis (SA) in
one of the Asian regional languages known as Marathi in this study by providing
a benchmark setup in which we first produced an annotated dataset composed of
Marathi text acquired from microblogging websites such as Twitter. We also
choose domain experts to manually annotate Marathi microblogging posts with
positive, negative, and neutral polarity. In addition, to show the efficient use of
the annotated dataset, an ensemble-based model for sentiment analysis was cre-
ated. In contrast to others machine learning classifier, we achieved better perfor-
mance in terms of accuracy for ensemble classifier with 10-fold cross-validation
(cv), outcomes as 97.77%, f-score is 97.89%.
Keywords: Sentiment analysis; machine learning; lexical resource; ensemble classifier
1 Introduction
In this digital age, millions of people are connected to one another through Web 2.0 and social
networking. This allows for a new technique of exchanging knowledge with other people. Social
networking sites, e-commerce websites, blogging, and other similar platforms allow users to instantly
generate creative content, thoughts, and opinions, leading in the development of huge amounts of data
every day. Sentiment analysis and opinion mining have grown as a challenging and dynamic field of
research for both resourced and under-resourced languages. The term sentiment refers to a broad concept
that encompasses sentiment, evaluation, appraisal, or attitude toward a piece of information that
demonstrates the author’s point of view.
This work is licensed under a Creative Commons Attribution 4.0 International License, which
permits unrestricted use, distribution, and reproduction in any medium, provided the original
work is properly cited.
Computer Systems Science & Engineering
DOI: 10.32604/csse.2023.027979
Article
ech
T
PressScience

Opinion mining or emotional intelligence are terms used to describe sentiment analysis. Sentiment
analysis is the systematic process of extracting useful knowledge from unstructured and unorganized text
information in various social platforms and online sources, such as chats on Twitter, WhatsApp, and
Facebook, as well as online blogs and comments. Opinion mining includes establishing automated
systems that employ any of the machine learning methods to accomplish opinion mining.
The number of Marathi internet users and web content has grown tremendously. Because Marathi is still
an under-resourced language in the field of sentiment analysis, there have been few attempts to perform SA in
Marathi. Users express their opinions in a variety of methods, including bilingual text, transliterated words,
emoticons, spelling variations, incorrect linguistic structures, and many others [1]. This makes sentiment
analysis a difficult field for research, particularly with Indian languages. This allows for the development
of Marathi resources and research in the field of sentiment analysis.
The major contributions of this research work are the development and evaluation of lexical resources
for sentiment analysis in Marathi, because there are minor lexical resources, libraries, and lexical Corpus
available for Marathi, indicating that Marathi has not been explored in the field of sentiment analysis. In
this research, we present an ensemble-based model for predicting the sentiment of Marathi texts through
integrating the output of Machine Learning-based models. And for developing benchmark dataset, we
manually annotated the Twitter dataset with the help of human annotators (domain experts), who are
senior researchers in Marathi, and for analysis of these annotators’performance, we used Fleiss’kappa as
performance measurement matrices, and lastly, all classification algorithms are also evaluated and
discussed. In addition, an annotated dataset of Marathi tweets with positive, negative, and neutral
sentiment orientations was created.
2 Related Work
In recent years, only a few Indian languages have been studied, including Hindi, Telugu, Tamil, Telugu,
and Bengali. However, as Indian people’s digital literacy grows and technology becomes easier to utilize for
creating content in Indian languages, countries like India will be capable of creating content in regional
languages on the Internet.
Authors have proposed ensemble-based model sentiment analysis of Persian text [2–4]. Sentiment
analysis is performed using deep learning and shallow approaches. In experimentation, achieved accuracy
is up to 79.68% [5]. Researchers proposed an ensemble-based recommender system for hotel reviews and
also categorized aspects [6]. And used ensemble of binary classification known as BERT technique, with
features as Word2Vec, subjectivity score and Term Frequency-Inverse Document Frequency (TF-IDF),
achieved performance of model with 84% f-score and 93.26% accuracy [7]. In proposed ensemble model
for feature extraction author has considered Information Gain (IG), Gini Index and Chi Square. And used
machine learning algorithms as Sequential Minimal Optimization (SMO), Multi-nominal Naïve Bayes
(MNB), and Random Forest (RF) and considered multi-domain dataset.
The researchers studied the use of Naive Bayes (NB) and Support Vector Machine for machine learning-
based sentiment classification of movie reviews (SVM) [8–12]. Sentiment Analysis is a two-class
classification problem comprising Positive and Negative classes; this kind of study may be used to
classify textual information and feature selection affects classifier performance.
The Authors have performed comparative performance weight of each binary classifier in the training
sample set is computed for enhanced one-vs-one (OVO) technique based on the K nearest neighbours and
the class centre of each category in the training sample set about the classification algorithm [13]. The
information gain (IG) approach is used to identify the key features for multi-class sentiment analysis; a
binary SVM classifier is then trained on feature extraction training of every pair of sentiment categories.
2458 CSSE, 2023, vol.44, no.3

Ensemble approaches, as alternative to using each of the individual learning algorithms alone, employ many
learning algorithms to achieve greater efficiency [14]. Deep learning techniques’performance can be
improved by combining them with standard approaches based on manually acquired features [15].
Machine Learning based techniques has played a significant role in Natural Language Processing [16].
Machine learning techniques are divided into two learning classes as supervised and unsupervised learning. For
task of Sentiment analysis mostly preferred supervised algorithms as Support Vector Machine (SVM),
Maximum Entropy and Naïve Bayes (NB) [17–19]. It includes feature-based sentiment analysis and summarization.
3 System Development
This section describes corpus creation process, pre-processing, manual annotation, and performance
evaluation of human annotator with the help of Fleiss’s Kappa [20]. And proposed ensemble-based model
for sentiment classification.
3.1 Corpus Creation
3.1.1 Corpus Acquisition
We have extracted publicly available Marathi Tweets from twitter with the help twitter-API. Initially, we
have collected generalized 1493 Marathi Tweets.
3.1.2 Data Preprocessing
Initially, pre-processed the data into the necessary forms, for which following steps are carried out:
Identified and eliminated duplicate and irrelevant tweets manually.
Identified and transliterated English words present in tweets into Marathi manually.
Removed stop words.
Performed lemmatization to find root word.
Removed any incorrect punctuation, smileys, hashtags, or photo tags.
Removed complicated sentences since they are inappropriate for performing sentiment analysis.
3.1.3 Data Annotation
We chose three domain experts who are senior scholars with a Ph.D. in Marathi to do manual data
annotation with the help of human annotators. We asked them to tag the Marathi Tweets dataset with 1,
0, and −1 to represent the positivity, neutrality, and negativity of Marathi tweets.
3.2 Feature Extraction
Supervised Machine learning methods generates output for test data by learning from a pre-defined set of
features in the training samples [21]. As Machine learning methods cannot directly works on raw text, as
result feature extraction methods are required to transfer text into a vector of features. In this research
work we are considering unigram with Term Frequency–Inverse Document Frequency (TF-IDF) for
feature extraction. Mostly, unigram i.e., single words hold important opinions, emotion [22]. For
example, “Camera of this mobile is good”, here word “good”expresses opinion about camera. So, it
becomes important for to consider Unigram + TF-IDF model for feature extraction.
The unigram word vectors obtained during initial stage are used to build a matrix containing all of the
tweets, and the unigrams recovered from the matrix are handled as features. The TF-IDF feature matrix is
constructed with the features as columns and tweets as rows. The Lexical TF-IDF is calculated by
multiplying each feature column of the TF-IDF feature matrix by its sentiment score. This matrix is used
to train supervised machine learning algorithms.
CSSE, 2023, vol.44, no.3 2459

3.3 Sentiment Classification Approach
To learn and classify, machine learning algorithms employ various series. The names of the input feature
vectors and their classes are included in the training set. Using this training set, a classification model was
created to classify the input material into positive and negative class [23]. Extracted feature sets are applied to
train the classifier to evaluate if the data set review is positive or negative. Ensemble techniques are a type of
machine learning methodology that integrates numerous base models to create a single best prediction model.
3.3.1 Logistic Regression (LR)
Logistic regression estimates probabilities using a logistic function, which is the cumulative logistic
distribution, to assess the association between a categorical dependent variable and one or more
independent variables [24–28]. Logistic regression is a linear approach; however, the logistic function is
used to modify the predictions. It is a statistical technique for assessing a dataset that has one or more
independent variables that affect the outcomes.
Instead of fitting a regression line, we fit a "S" shaped logistic function that predicts two maximum
values in logistic regression (0 or 1). Logistic regression starts with a conventional linear regression and
then adds a sigmoid to the linear regression result. Regression is expressed Eq. (1) and logistic function
in Eq. (2).
¼w0x0þw1x1þw2x2þwNxN(1)
where, w
0
indicates weights and x1 represents independent variables.
hzðÞ¼ 1
1þexpz(2)
3.3.2 Stochastic Gradient Decent (SGD)
Stochastic Gradient Descent (SGD) is a straightforward but highly efficient method for fitting linear
classifiers and regressors to convex loss functions. SGD has been effectively used to large-scale and
sparse machine learning applications, such as text categorization and NLP. Given the sparsity of the data,
the classifiers in this module can efficiently scale to problems with more than training instances and more
than features. The class SGD Classifier provides a simple stochastic gradient descent learning process that
supports various classification loss functions and penalties. The decision boundary of an SGD Classifier
trained with the hinged loss, which is comparable to a linear SVM.
3.3.3 Support Vector Machine (SVM)
The Support Vector Machine (SVM) is a well-known supervised machine learning model for
categorization and prediction of different datasets. Several studies claim that SVM is a fairly accurate
approach for text categorization. It is also often used in sentiment analysis.
For example, if we have a dataset with data that has been pre-labelled into two categories: positive and
negative labels in Fig. 1, we may train a model to classify real time data into these two categories. This is
precisely how SVM operates. We train the model on a dataset so that it can evaluate and classify
unknown data into the categories that were present in the training set.
3.3.4 Naive Bayes (NB)
The Naive Bayes classifier is a prominent supervised classifier that allows you to express positive,
negative, and neutral sentiments in content. To classify words into their respective categories, the Naive
Bayes classifier employs conditional probability. The advantage of using Naive Bayes for text
classification is that it just requires a minimal dataset for training. The raw data is pre-processed, with
removal of stop words, punctuation marks, extra spaces, transliteration of other language words and
2460 CSSE, 2023, vol.44, no.3

special symbols removed. Human annotator performs the manual tagging of words with labels of positive,
negative, and neutral tags.
It can be beneficial for determining the likelihood of each statement using sentiment. In this technique,
each attribute helps to selecting which labelling should be allocated to the emotion value of each phrase. The
Naive Bayes classifier starts by computing the prior probability of each labelled sentence, which is derived by
examining the occurrence of each labelled statement in the training data set. Following Eq. (3) describes
bayes rule.
PAjBðÞ¼
PðBjAÞPAðÞ
PBðÞ (3)
where, A is Particular class, B sentence which needs to be classified, P(A) and P(B) are Prior
probabilities, and P(A | B) and P(B | A) are Posterior probabilities.
3.3.5 Nearest Neighbour
Nearest Neighbours (KNN) is an important classification technique in Machine Learning. It is a
supervised learning algorithm that is widely used in text classification. It is extensively applicable in real-
world circumstances since it is non-parametric, which means it makes no underlying assumptions
regarding data distribution. We are provided some previous data (also known as training data) that
classify locations into categories based on a characteristic.
3.3.6 Ensemble Classifier
The purpose of Ensemble techniques is to integrate the predictions of numerous base estimators with a
specific learning algorithm to increase the classifier’s accuracy and resilience. The idea behind the Voting
Classifier is to integrate conceptually distinct machine learning classifiers and forecast the class labels
using a majority vote or the average projected probability (soft vote). Such a classifier can be effective for
balancing out the individual flaws of a set of similarly highly performing classifiers.
Fig. 2 shows An Ensemble based Sentiment classification approach using supervised Machine Learning
algorithms. And Algorithms are Support Vector Machine (SVM), Nave Bayes (NB), k-Nearest Neighbour
(KNN), Neural Network, Decision Tree (DT), Logistic Regression (LR), Stochastic Gradient Decent
(SGD), and the proposed Ensemble-based Model are implemented in research work.
Figure 1: Optimal hyperplane
CSSE, 2023, vol.44, no.3 2461

Algorithm: Sentiment analysis using proposed ensemble based algorithm
Input: An annotated Marathi Tweet Dataset
A list of tweets, Tcontains positive and negative sentences
T={T
p1
,T
P2
,……,T
pi
,T
n1
,T
n2
,……,T
ni
}
Where, T
pi
is number of positive sentences, and T
ni
is number of negative sentences
Pos_count,Neg_count is Positive count and negative count respectively.
Output: Pos_score
i
, Neg_score
i
contains sentiment score
Sentiment_polarity_score(dataset)
1. for each T
i
,in Tdo
2. Pos_count
i
=0;
3. Neg_count
i
=0;
4. for each Classifier_C
i
in ensemble_classifier do
5. if C
i
predict_positive then
6. Pos_count
i
+= 1;
7. else
8. Neg_count
i
+= 1;
9. [if-else end]
10. [for end]
11. [for end]
12. for each Classifier_C
i
in ensemble_classifier do
13. Weight
Ci
= accuracy_C
i
/ (Sum of all learning
classifiers in ensemble classifier)
14. [for end]
15. for each T
i
,in Tdo
16. Pos_count
i
=0;
17. Neg_count
i
=0;
18. for each Classifier_C
i
in ensemble_classifier do
19. if C
i
predict_positive then
20. Pos_score
i
+= Weight
Ci
* prob(pos
i
);
21. else
22. Neg_score
i
+= Weight
Ci
* prob(neg
i
);
(Continued)
2462 CSSE, 2023, vol.44, no.3

4 Performance Evaluation
4.1 Data Annotation: Inter-annotator Agreement Score
We employed the Fleiss’Kappa inter annotator agreement score to evaluate manual data annotation
evaluation between annotator. Fleiss’kappa score is calculated using the formula below (Wik21).
k¼

Px

Px
1

Px
(4)
Where, the factor 1 

Pxrepresents the degree of agreement that can be obtained other than by chance,
The degree of agreement that was achieved above chance is given by

Px

Px. and if the evaluators are totally
in agreement, Kappa k = 1 and k = 0 if there is no agreement among the evaluators (other than what would be
expected by chance). And for Marathi Tweets dataset the inter-annotator agreement score is k = 0.957, which
is almost perfect agreement. Tab. 1. Inter-Annotator agreement score shows Inter-Annotator agreement score
and Tab. 2. The statistics for Marathi tweets dataset after preprocessing and data annotation. shows the
Algorithm: (continued)
23. [if-else end]
24. [for end]
25. return Pos_score
i,
Neg_score
i
26. [for end]
Calculating Probability:
prob posi
ðÞ¼ Pos counti
Pos countiþNeg counti
prob negi
ðÞ¼ Neg counti
Pos countiþNeg counti
Figure 2: Ensemble based sentiment classification approach
CSSE, 2023, vol.44, no.3 2463

statistics for Marathi tweets dataset after preprocessing and data annotation. Inter- Annotator agreement score
and the statistics for Marathi tweets dataset after preprocessing and data annotation are shown in graphical
manner in Figs. 3 and 4. respectively.
Table 1: Inter-annotator agreement score
Annotator ( iwith j) Fleiss’s Kappa score
A
12
0.953
A
23
0.965
A
13
0.954
Avg. agreement score 0.957
Table 2: The statistics for marathi tweets dataset after preprocessing and data annotation.
Sr. no. Statistics No. of tweets
1 Initial 1493
2 After Preprocessing 1069
3 Positive 627
4 Negative 430
5 Neutral 12
Figure 3: Graphical representation of inter-annotator agreement score (fleiss’s kappa)
Figure 4: Graphical representation of statistics for marathi tweets dataset
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4.2 Performance of Sentiment Classification Approach
We concentrated on three sorts of class problems in the experiment: positivity, neutrality, and negativity.
Using the Twitter API, we retrieved Marathi tweets. Furthermore, the Marathi Tweets dataset is classified into
three groups depending on the sentiment represented in the statements. If the expressed attitude indicates
positivity, then labelled as 1, if it is neutrality then labelled as 0, and if it is negativity then labelled as −1.
The dataset is partitioned into 75:25 ratios for training and testing datasets. The dataset is subjected to
different preprocessing methods, including data cleaning, URL and Hashtag removal, unnecessary blank
spaces, emojis, removal of Stopword, and lemmatization. k-fold cross validation with k = 5 and
k = 10 was also employed.
And evaluation metrics used are F-score and Accuracy which are calculated as below.
Recall ¼TP Sentiment
TP Sentiment þFN Sentiment (5)
Precision ¼TP Sentiment
TP Sentiment þFP Sentiment (6)
FScore ¼2Precision Recall
Precision þRecall (7)
Accuracy ¼TP Sentiment þTN Sentiment
TP Sentiment þFN Sentiment þFP Sentiment þTN Sentiment (8)
Analyzed comparative results from base classifiers, majority voting ensemble, and developed ensemble
classifier. The proposed ensemble classifier’s performance is compared to that of the individual conventional
classifier and the majority voting ensemble classifier. Tab. 3. displays the results. On Marathi datasets, the
suggested ensemble classifier outperformed the stand-alone classifier and the majority voting ensemble
classifier.
A classification model may be assessed using a variety of metrics, the most basic of which is accuracy
and f-score. Tab. 3. shows the performance evaluation of individual classifier with k-fold validation.
Graphical representation of performance evaluation of individual classifier with k-fold validation is shown
in Figs. 5 and 6.
Table 3: Performance evaluation of individual classifier with k-fold validation
Sr. no. Classifier Unigram + TF-IDF ( k=5) Unigram + TF-IDF ( k=10)
Accuracy F-score Accuracy F-score
1 SVM 92.46% 96.00% 91.89% 95.65%
2 Multinomial Naïve Byes 90.76% 95.15% 89.53% 94.44%
3 K-Nearest Neighbour 91.98% 95.70% 89.63% 94.35%
4 Neural Network 93.40% 96.44% 92.83% 96.01%
5 Decision Tree 91.71% 91.84% 90.90% 92.94%
6 Logistic Regression 90.76% 95.15% 88.97% 94.15%
7 Stochastic Gradient Decent 95.47% 97.37% 96.13% 97.62%
8 Ensemble Classifier 96.77% 98.73% 97.77% 97.89%
CSSE, 2023, vol.44, no.3 2465

We performed 5-fold cross validation (cv) on dataset, for individual classifier Support Vector Machine
(SVM), Multinomial Naïve Bayes (MNB), K- Nearest Neighbour (KNN), Neural Network (ANN), Decision
Tree (DT), Logistic Regression (LR), Stochastic Gradient Decent (SGD), we obtained accuracy as 92.46%,
90.76%, 91.98%, 93.40%, 91.71%, 90.76%, and 95.47% respectively and obtained better performance in
terms of accuracy for ensemble classifier as 96.77%, f-score is 98.73%. For 10-fold cross validation (cv)
on dataset, individual classifier SVM, MNB, KNN, ANN, DT, LR, and SGD, we obtained accuracy as
91.89%, 89.53%, 89.63%, 92.83%, 90.90%, 88.97%, and 96.13%, respectively and we obtained better
accuracy for ensemble classifier as 97.77%, f-score is 97.89% for Marathi tweets dataset.
4.3 Result Discussions
This is the first attempt to develop and evaluate a machine learning-based ensemble classifier for
Marathi, and because there are no results for the same language, we compared our model with Hindi and
Konkani for result analysis because these languages are considered for sentiment analysis using Machine
Learning algorithms, and they are also in the Devanagari language family. The authors employed
Figure 5: Graphical representation of performance evaluation of individual classifier with 5-fold validation
Figure 6: Graphical representation of performance evaluation of individual classifier with 10-fold validation
2466 CSSE, 2023, vol.44, no.3

machine learning techniques such as Naive Bayes, Decision Tree, and Support Vector Machine (SMO) using
the Weka tool to reach accuracy of 50.95%, 54.48%, and 51.07% for the electronics product review dataset in
Hindi [25]. In the case of Konkani, the authors used a dataset of Konkani poetry with Naive Bayes
classification and attained an accuracy of 82.67% [26–28]. Furthermore, we have obtained better
classification results for ensembled based classifier as 96.77%, 97.77%, for 5-fold and 10-fold cv
respectively.
5 Conclusions
This research work presents a benchmarked technique for Sentiment Analysis of an Asian language
“Marathi”. For which we created an annotated corpus of Marathi Tweets, and performed manual data
annotation with the help of domain experts with tweets labelled as positivity, neutrality and negativity
polarity score that is 1, 0, and −1. And for performance evaluation of manually annotated corpus we used
Fleiss’s kappa (Inter-annotator agreement score) metrics and achieved average kappa score k = 0.957,
which is almost perfect agreement between inter-annotator. For ensemble-based Sentiment classification
experimentation, obtained better performance in terms of accuracy for ensemble classifier with 5-fold
cross validation (cv) 96.77%, f-score is 98.73% and with 10-fold cross validation (cv), we obtained better
accuracy for ensemble classifier as 97.77%, f-score is 97.89% for Marathi tweets dataset in comparison
with another machine learning classifier.
Acknowledgement: The authors wish to express their thanks to one and all who supported them during
this work.
Funding Statement : This paper was supported by Wonkwang University in 2022.
Conflicts of Interest: The authors declare that they have no conflicts of interest to report regarding the
present study.
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