Accepted 9 March 2021
Available Online 22 March 2021
- Sentiment analysis
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.
- © 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 -