CFP last date
20 May 2024
Reseach Article

Identifying and Categorizing Opinions Expressed in Bangla Sentences using Deep Learning Technique

by Moqsadur Rahman, Summit Haque, Zillur Rahman Saurav
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 176 - Number 17
Year of Publication: 2020
Authors: Moqsadur Rahman, Summit Haque, Zillur Rahman Saurav
10.5120/ijca2020920119

Moqsadur Rahman, Summit Haque, Zillur Rahman Saurav . Identifying and Categorizing Opinions Expressed in Bangla Sentences using Deep Learning Technique. International Journal of Computer Applications. 176, 17 ( Apr 2020), 13-17. DOI=10.5120/ijca2020920119

@article{ 10.5120/ijca2020920119,
author = { Moqsadur Rahman, Summit Haque, Zillur Rahman Saurav },
title = { Identifying and Categorizing Opinions Expressed in Bangla Sentences using Deep Learning Technique },
journal = { International Journal of Computer Applications },
issue_date = { Apr 2020 },
volume = { 176 },
number = { 17 },
month = { Apr },
year = { 2020 },
issn = { 0975-8887 },
pages = { 13-17 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume176/number17/31292-2020920119/ },
doi = { 10.5120/ijca2020920119 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:42:46.976173+05:30
%A Moqsadur Rahman
%A Summit Haque
%A Zillur Rahman Saurav
%T Identifying and Categorizing Opinions Expressed in Bangla Sentences using Deep Learning Technique
%J International Journal of Computer Applications
%@ 0975-8887
%V 176
%N 17
%P 13-17
%D 2020
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Identifying and categorizing opinions in a sentence is the most prominent branch of natural language processing. It deals with the text classification to determine the intention of the author of the text. The intention can be for the presentation of happiness, sadness, patriotism, disgust, advice, etc. Most of the research work on opinion or sentiment analysis is in the English language. Bengali corpus is increasing day by day. A large number of online News portals publish their articles in Bengali language and a few News portals have the comment section that allows expressing the opinion of people. Here a research work has been done on Bengali Sports news comments published in different newspapers to train a deep learning model that will be able to categorize a comment according to its sentiment. Comments are collected and separated based on immanent sentiment. The deep learning algorithms that have been used are Convolutional Neural Network (CNN), Multilayer Perceptron, Long Short-Term Memory (LSTM).

References
  1. Das, D., and Bandyopadhyay, S. 2010. Finding emotion holder from Bengali blog texts -An unsupervised syntactic approach. PACLIC 24 - Proceedings of the 24th Pacific Asia Conference on Language, Information and Computation.
  2. Sarkar, Iqbal, A., Pavel, D. S. H., and Khan, M. 2007, Automatic Bangla corpus creation. Center for research on Bangla language processing (CRBLP), BRAC University.
  3. Hasan, Muhammad, F., UzZaman, N., and Khan, M. 2007. Comparison of different POS Tagging Techniques (N-Gram, HMM and Brill’s tagger) for Bangla. Advances and Innovations in Systems, Computing Sciences and Software Engineering.
  4. Majumder, K. M. Y. A., Islam, M. Z., UzZaman, N., and Khan, M. Analysis of and Observations from a Bangla news Corpus. Center for Research on Bangla Language Processing, BRAC University, Dhaka, Bangladesh.
  5. Nabi, M. M., Altaf, M. T., and Ismail, S. 2016. Detecting Sentiment from Bangla Text using Machine Learning Technique and Feature Analysis International Journal of Computer Applications (0975 – 8887) Volume 153 – No 11.
  6. Ghosal, T., Das, S. K., and Bhattacharjee, S. Sentiment Analysis on বাংলা রাশিফল (Bengali Horoscope) Corpus.
  7. Chowdhury, S., and Chowdhury, W. 2014. Performing sentiment analysis in Bangla microblog posts. Int. Conf. Informatics, Electron. Vision, ICIEV
  8. Ghosal, T., Das, S. K., and Bhattacharjee, S. 2016. Sentiment analysis on (Bengali horoscope) corpus. 12th IEEE Int. Conf. Electron. Energy, Environ. Commun. Comput. Control (E3-C3), INDICON 2015, pp. 1–6.
  9. Trivedi, M., Soni, N., Sharma, S., and Nair, S. 2015. Comparison of Text Classification Algorithms. International Journal of Engineering Research & Technology (IJERT). vol. 4, no. 2, pp. 334–336.
  10. Mishu, S. Z., and Rafiuddin, S. M. 2016. Performance Analysis of Supervised Machine Learning Algorithms for Text Classification. 201619th Int. Conf. Comput. Inf. Technol., pp. 409–413.
  11. Paul, Kumar, A., and Shill, P. C. 2016. Sentiment mining from Bangla data using mutual information. Electrical, Computer & Telecommunication Engineering (ICECTE), International Conference on. IEEE, 2016.
  12. Islam, M. S., Islam, M. A., Hossain, M. A., and Dey, J. J. 2016. Supervised approach of sentimentality extraction from bengali facebook status. In Computer and Information Technology (ICCIT), 2016 19th International Conference on, pp. 383-387. IEEE.
  13. Islam, M. S., Al-Amin, M., and Uzzal, S. D. 2016. Word embedding with hellinger PCA to detect the sentiment of bengali text. Computer and Information Technology (ICCIT), 2016 19th International Conference on. IEEE.
  14. Akhtar, M. S., Kumar, A., Ekbal, A., and Bhattacharyya, P. 2016. A hybrid deep learning architecture for sentiment analysis. In Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pp. 482-493.
Index Terms

Computer Science
Information Sciences

Keywords

CNN LSTM ROC curve Confusion Matrix Performance Analysis