A Systematic Review of Sentiment Analysis from Bengali Text using NLP
DOI:
https://doi.org/10.54536/ajaset.v6i3.990Keywords:
Bangla, NLP, Review, Sentiment, SurveyAbstract
Sentiment Analysis (SA) is the method of studying a person’s comments and statements through computational means. It is a sub-domain of Natural Language Processing. A sentiment analysis (SA) system is created by training a significant number of positive, negative or neutral sentence datasets. Although there are many research papers on this subject in English, the work of sentiment analysis in Bengali has not become very popular due to the complexity of the Bangla language and the insufficient presence of the Bangla language online. But now the use of Bengali language has increased in online news, social media, and blogs. At the same time, the number of researches on Bengali NLP is also increasing. But what is the current state of sentiment analysis, its limitations and is there still room for improvement in some places, are not being properly reviewed and research is lagging behind. We have conducted a review on sentiment analysis so that future researchers of sentiment analysis can easily find out about the current state of sentiment analysis. In this research, we have tried to survey the current context of sentiment analysis (SA) and at the same time, we have created a sequence of comparatively better research from the existing ones. To create this sequence we followed a method called is TOPSIS. We have also discussed the challenges to overcome for improving the Sentiment analyzer.
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Alam, M. H., Rahoman, M. M., & Azad, M. A. K. (2018). Sentiment analysis for Bangla sentences using convolutional neural network. 20th International Conference of Computer and Information Technology, ICCIT 2017, 1–6. https://doi.org/10.1109/ICCITECHN.2017.8281840
Arafin Mahtab, S., Islam, N., & Mahfuzur Rahaman, M. (2018). Sentiment Analysis on Bangladesh Cricket with Support Vector Machine. 2018 International Conference on Bangla Speech and Language Processing, ICBSLP, 1–4. https://doi.org/10.1109/ICBSLP.2018.8554585
Aziz Sharfuddin, A., Nafis Tihami, M., & Saiful Islam, M. (2018). A Deep Recurrent Neural Network with BiLSTM model for Sentiment Classification. 2018 International Conference on Bangla Speech and Language Processing, ICBSLP 2018, September. https://doi.org/10.1109/ICBSLP.2018.8554396
Chavan, G. S., Manjare, S., Hegde, P., & Sankhe, A. (2014). A Survey of Various Machine Learning, 15(6), 288–292.
Cho, K., Van Merriënboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., & Bengio, Y. (2014). Learning phrase representations using RNN encoder-decoder for statistical machine translation. 2014 Conference on Empirical Methods in Natural Language Processing, Proceedings of the Conference, 1724–1734. https://doi.org/10.3115/v1/d14-1179
Chowdhury, S., & Chowdhury, W. (2014). Performing sentiment analysis in Bangla microblog posts. 2014 International Conference on Informatics, Electronics and Vision, ICIEV 2014. https://doi.org/10.1109/ICIEV.2014.6850712
Dash, N. S., & Ramamoorthy, L. (2018). Utility and application of language corpora. Utility and Application of Language Corpora, Sasaki 2003, 1–290. https://doi.org/10.1007/978-981-13-1801-6
De Silva, J., & Haddela, P. S. (2013). A term weighting method for identifying emotions from text content. 2013 IEEE 8th International Conference on Industrial and Information Systems, ICIIS 2013-Conference Proceedings, 381–386. https://doi.org/10.1109/ICIInfS.2013.6732014
Dey, R. C., & Sarker, O. (2019). Sentiment analysis on bengali text using lexicon based approach. 2019 22nd International Conference on Computer and Information Technology, ICCIT 2019, 1–5. https://doi.org/10.1109/ICCIT48885.2019.9038250
Fukushima, K. (1988). Neocognitron: A hierarchical neural network capable of visual pattern recognition. Neural Networks, 1(2), 119–130. https://doi.org/10.1016/0893-6080(88)90014-7
GitHub-atikdu/Bangla_ABSA_Datasets.(n.d.). Retrieved May 29, 2022, from https://github.com/atikdu/Bangla_ABSA_Datasets
Hao, J., & Ho, T. K. (2019). Machine Learning Made Easy: A Review of Scikit-learn Package in Python Programming Language. Journal of Educational and Behavioral Statistics, 44(3), 348–361. https://doi.org/10.3102/1076998619832248
Hasan, K. M. A., Rahman, M., & Badiuzzaman. (2014). Sentiment detection from Bangla text using contextual valency analysis. 2014 17th International Conference on Computer and Information Technology, ICCIT 2014, 292–295. https://doi.org/10.1109/ICCITechn.2014.7073151
Hassan, A., Amin, M. R., Azad, A. K. Al, & Mohammed, N. (2017). Sentiment analysis on bangla and romanized bangla text using deep recurrent models. IWCI 2016 - 2016 International Workshop on Computational Intelligence, 51–56. https://doi.org/10.1109/IWCI.2016.7860338
Hochreiter, S., & Schmidhuber, J. (1997). Long Short-Term Memory. Neural Computation, 9(8), 1735–1780. https://doi.org/10.1162/neco.1997.9.8.1735
Hossain, E., Sharif, O., & Moshiul Hoque, M. (2021). Sentiment Polarity Detection on Bengali Book Reviews Using Multinomial Naïve Bayes. Advances in Intelligent Systems and Computing, 281–292. https://doi.org/10.1007/978-981-33-4299-6_23
Islam, K. I., Islam, M. S., & Amin, M. R. (2020). Sentiment analysis in Bengali via transfer learning using multi-lingual BERT. ICCIT 2020 - 23rd International Conference on Computer and Information Technology, Proceedings, 19–21. https://doi.org/10.1109/ICCIT51783.2020.9392653
Islam, M. S., Islam, M. A., Hossain, M. A., & Dey, J. J. (2017). Supervised Approach of sentimentality extraction from Bengali facebook status. 19th International Conference on Computer and Information Technology, ICCIT 2016, March 2018, 383–387. https://doi.org/10.1109/ICCITECHN.2016.7860228
Keras: the Python deep learning API. (n.d.). Retrieved August 29, 2022, from https://keras.io/
Khan, M. R. H., Afroz, U. S., Masum, A. K. M., Abujar, S., & Hossain, S. A. (2020). Sentiment Analysis from Bengali Depression Dataset using Machine Learning. 2020 11th International Conference on Computing, Communication and Networking Technologies, ICCCNT 2020. https://doi.org/10.1109/ICCCNT49239.2020.9225511
Mahmudun, M., Tanzir, M., & Ismail, S. (2016). Detecting Sentiment from Bangla Text using Machine Learning Technique and Feature Analysis. International Journal of Computer Applications, 153(11), 28–34. https://doi.org/10.5120/ijca2016912230
Mandal, P., & M Mainul Hossain, B. (2017). A Systematic Literature Review on Spell Checkers for Bangla Language. International Journal of Modern Education and Computer Science, 9(6), 40–47. https://doi.org/10.5815/ijmecs.2017.06.06
Md Al-Amin, Islam, M. S., & Das Uzzal, S. (2017). Sentiment analysis of Bengali comments with Word2Vec and sentiment information of words. ECCE 2017 - International Conference on Electrical, Computer and Communication Engineering, 186–190. https://doi.org/10.1109/ECACE.2017.7912903
Mikolov, T., Chen, K., Corrado, G., & Dean, J. (2013). Efficient estimation of word representations in vector space. 1st International Conference on Learning Representations, ICLR 2013 - Workshop Track Proceedings, 1–12.
Mohammad, S. M. (2016). Sentiment Analysis: Detecting Valence, Emotions, and Other Affectual States from Text. Emotion Measurement, 201–237. https://doi.org/10.1016/B978-0-08-100508-8.00009-6
Prasad, S. S., Kumar, J., Prabhakar, D. K., & Tripathi, S. (2017). Sentiment mining: An approach for Bengali and Tamil tweets. 2016 9th International Conference on Contemporary Computing, IC3 2016, 1–4. https://doi.org/10.1109/IC3.2016.7880246
Qiu, R., & Li, D. (2016). The Importance of Neutral Class in Sentiment Analysis of ArabicTweets, 8(2), 17–31. https://doi.org/10.5121/ijcsit.2016.8202
Rabeya, T., Chakraborty, N. R., Ferdous, S., Dash, M., & Al Marouf, A. (2019). Sentiment Analysis of Bangla Song Review- A Lexicon Based Backtracking Approach. Proceedings of 2019 3rd IEEE International Conference on Electrical, Computer and Communication Technologies, ICECCT 2019, 1–7. https://doi.org/10.1109/ICECCT.2019.8869290
Saaty, T. L. (2002). Decision making with the Analytic Hierarchy Process. Scientia Iranica, 9(3), 215–229. https://doi.org/10.1504/ijssci.2008.017590
Saranya, K., & Jayanthy, S. (2018). Onto-based sentiment classification using machine learning techniques. Proceedings of 2017 International Conference on Innovations in Information, Embedded and Communication Systems, ICIIECS 2017, 2018-Janua, 1–5. https://doi.org/10.1109/ICIIECS.2017.8276047
Sarkar, K., & Bhowmick, M. (2018). Sentiment polarity detection in Bengali tweets using multinomial Naïve Bayes and support vector machines. 2017 IEEE Calcutta Conference, CALCON 2017 - Proceedings, 2018-Janua, 31–35. https://doi.org/10.1109/CALCON.2017.8280690
Savanur, S. R., & Sumathi, R. (2018). Feature Based Sentiment Analysis of Compound Sentences. 2017 2nd International Conference On Emerging Computation and Information Technologies, ICECIT 2017, 1–6. https://doi.org/10.1109/ICECIT.2017.8453357
Sazzed, S., & Jayarathna, S. (2019). A sentiment classification in bengali and machine translated english corpus. Proceedings - 2019 IEEE 20th International Conference on Information Reuse and Integration for Data Science, IRI 2019, 107–114. https://doi.org/10.1109/IRI.2019.00029
Sharif, O., Hoque, M. M., & Hossain, E. (2019). Sentiment Analysis of Bengali Texts on Online Restaurant Reviews Using Multinomial Naïve Bayes. 1st International Conference on Advances in Science, Engineering and Robotics Technology 2019, ICASERT 2019, 1–6. https://doi.org/10.1109/ICASERT.2019.8934655
Siddiqi Emon, M. I., Ahmed, S. S., Milu, S. A., & Mahtab, S. S. (2019). Sentiment Analysis of Bengali Online Reviews written with English Letter Using Machine Learning Approaches. ACM International Conference Proceeding Series, 109–115. https://doi.org/10.1145/3362966.3362977
‘snltr-software’ [online]. available: http://nltr.org/snltr-software/ [accessed: - Google Search. (n.d.). Retrieved May 29, 2022, from https://www.google.com/search?
Tabassum, N., & Khan, M. I. (2019). Design an Empirical Framework for Sentiment Analysis from Bangla Text using Machine Learning. 2nd International Conference on Electrical, Computer and Communication Engineering, ECCE 2019, 1–5. https://doi.org/10.1109/ECACE.2019.8679347
Tripto, N. I., & Ali, M. E. (2018). Bangla YouTube Comments. 21–22.
Tuhin, R. A., Paul, B. K., Nawrine, F., Akter, M., & Das, A. K. (2019). An automated system of sentiment analysis from Bangla text using supervised learning techniques. 2019 IEEE 4th International Conference on Computer and Communication Systems, ICCCS 2019, September, 360–364. https://doi.org/10.1109/CCOMS.2019.8821658
Twitter NLP Example: How to Scale Part-of-Speech Tagging with MPP (Part 1). (n.d.). Retrieved August 30, 2022, from https://tanzu.vmware.com/content/blog/twitter-nlp-example-how-to-scale-part-of-speech-tagging-with-mpp-part-1
Witten, I. H., & Frank, E. (2005). Credibility: Evaluating What’s been Learned. In Data Mining: Practical machine learning tools and techniques. shorturl.at/efgX1
বাংলা ব্যাকরণ – Bangla Library. (n.d.). Retrieved May 29, 2022, from https://www.ebanglalibrary.com/category/বাংলা-ব্যাকরণ
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Copyright (c) 2023 Suma Hira, Mostafijur Rahman Akhond, Saumik Chowdhury, Atish Kumar Dipongkor, Syed Md.Galib
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