Natural Language Processing Research

Volume 1, Issue 3-4, March 2021, Pages 34 - 45

Bangla Text Sentiment Analysis Using Supervised Machine Learning with Extended Lexicon Dictionary

Authors
Nitish Ranjan Bhowmik1, Mohammad Arifuzzaman2, *, ORCID, M. Rubaiyat Hossain Mondal1, ORCID, M. S. Islam1
1 Institute of Information and Communication Technology (IICT), Bangladesh University of Engineering and Technology (BUET), Dhaka, Banglaadesh
2 Department of Electronics and Communications Engineering, East West University, Dhaka, Banglaadesh
*Corresponding author. Email: mazaman@ewubd.edu
Corresponding Author
Mohammad Arifuzzaman
Received 19 September 2020, Accepted 9 March 2021, Available Online 22 March 2021.
DOI
https://doi.org/10.2991/nlpr.d.210316.001How to use a DOI?
Keywords
Sentiment analysis, Bangla NLP, Tf-Idf, SVM, BTSC, N-grams, Bi-grams
Abstract

With the proliferation of the Internet's social digital content, sentiment analysis (SA) has gained a wide research interest in natural language processing (NLP). A few significant research has been done in Bangla language domain because of having intricate grammatical structure on text. This paper focuses on SA in the context of Bangla language. Firstly, a specific domain-based categorical weighted lexicon data dictionary (LDD) is developed for analyzing sentiments in Bangla. This LDD is developed by applying the concepts of normalization, tokenization, and stemming to two Bangla datasets available in GitHub repository. Secondly, a novel rule–based algorithm termed as Bangla Text Sentiment Score (BTSC) is developed for detecting sentence polarity. This algorithm considers parts of speech tagger words and special characters to generate a score of a word and thus that of a sentence and a blog. The BTSC algorithm along with the LDD is applied to extract sentiments by generating scores of the two Bangla datasets. Thirdly, two feature matrices are developed by applying term frequency-inverse document frequency (tf-idf) to the two datasets, and by using the corresponding BTSC scores. Next, supervised machine learning classifiers are applied to the feature matrices. Results show that for the case of BiGram feature, support vector machine (SVM) achieves the best classification accuracy of 82.21% indicating the effectiveness of BTSC algorithm in Bangla SA.

Copyright
© 2021 The Authors. Published by Atlantis Press B.V.
Open Access
This is an open access article under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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Journal
Natural Language Processing Research
Volume-Issue
1 - 3-4
Pages
34 - 45
Publication Date
2021/03/22
ISSN (Online)
2666-0512
DOI
https://doi.org/10.2991/nlpr.d.210316.001How to use a DOI?
Copyright
© 2021 The Authors. Published by Atlantis Press B.V.
Open Access
This is an open access article under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - JOUR
AU  - Nitish Ranjan Bhowmik
AU  - Mohammad Arifuzzaman
AU  - M. Rubaiyat Hossain Mondal
AU  - M. S. Islam
PY  - 2021
DA  - 2021/03/22
TI  - Bangla Text Sentiment Analysis Using Supervised Machine Learning with Extended Lexicon Dictionary
JO  - Natural Language Processing Research
SP  - 34
EP  - 45
VL  - 1
IS  - 3-4
SN  - 2666-0512
UR  - https://doi.org/10.2991/nlpr.d.210316.001
DO  - https://doi.org/10.2991/nlpr.d.210316.001
ID  - Bhowmik2021
ER  -