Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Published in:
Forecasting Price of Indian Stock Market Using Supervised Machine Learning Technique Read chapter Read first chapter

2021 | OriginalPaper | Chapter

Sentiment Polarity Detection on Bengali Book Reviews Using Multinomial Naïve Bayes

Authors : Eftekhar Hossain, Omar Sharif, Mohammed Moshiul Hoque

Published in: Progress in Advanced Computing and Intelligent Engineering

Publisher: Springer Singapore

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

Recently, sentiment polarity detection has increased attention to NLP researchers due to the massive availability of customer’s opinions or reviews in the online platform. Due to the continued expansion of e-commerce sites, the rate of purchase of various products, including books, is growing enormously among the people. Reader’s opinions/reviews affect the buying decision of a customer in most cases. This work introduces a machine learning-based technique to determine sentiment polarities (either positive or negative category) from Bengali book reviews. To assess the effectiveness of the proposed technique, a corpus with 2000 reviews on Bengali books is developed. A comparative analysis with various approaches (such as logistic regression, naive Bayes, SVM, and SGD) also performed by taking into consideration of the unigram, bigram, and trigram features, respectively. Experimental result reveals that the multinomial naive Bayes with unigram feature outperforms the other techniques with \(84\%\) accuracy on the test set.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

inform now
previous chapter Rumor Source Identification on Social Networks: A Combined Network Centrality Approach
next chapter Real-Time Facial Emotions Analysis in Videos
Literature
1.
Cambria, E., Grassi, M., Hussain, A., Havasi, C.: Sentic computing for social media mark theory. Multimedia Tools Appl. 59(2), 557–577 (2019)CrossRef
2.
Chowdhury, R.R., Hossain, M.S., Hossain, S., Andersson, K.: Analyzing sentiment of movie reviews in bangla by applying machine learning techniques. In: International Conference on Bangla Speech and Language Processing (2019)
3.
Dash, N.S., Ramamoorthy, Naicker, L.: Utility & Application of Language Corpora, pp. 17–34 (2019)
4.
Fang, X., Zhan, J.: Sentiment analysis using product review data. J. Big Data 2(1), 5 (2015)CrossRef
5.
Feng, Z.: Hot news mining and public opinion guidance analysis based on sentiment computing in network social media. Personal Ubiquitous Comput. 23, 373–381 (2019)CrossRef
6.
Ghosal, T., Das, S.K., Bhattacharjee, S.: Sentiment analysis on (Bengali horoscope) corpus. In: 2015 Annual IEEE India Conference (INDICON). pp. 1–6. IEEE (2015)
7.
Hammad, M., Al-awadi, M.: Sentiment analysis for Arabic reviews in social networks using machine learning. In: Information Technology: New Generations, pp. 131–139. Springer, Berlin (2016)
8.
Islam, M.S., Islam, M.A., Hossain, M.A., Dey, J.J.: Supervised approach of sentimentality extraction from Bengali Facebook status. In: 2016 19th International Conference on Computer and Information Technology (ICCIT), pp. 383–387. IEEE (2016)
9.
Krishna, A., Akhilesh, V., Aich, A., Hegde, C.: Sentiment analysis of restaurant reviews using machine learning techniques. In: Emerging Research in Electronics, Computer Science and Technology, pp. 687–696. Springer, Berlin (2019)
10.
Le, H.S., Van Le, T., Pham, T.V.: Aspect analysis for opinion mining of Vietnamese text. In: 2015 International Conference on Advanced Computing and Applications (ACOMP). pp. 118–123. IEEE (2015)
11.
Mahtab, S.A., Islam, N., Rahaman, M.M.: Sentiment analysis on bangladesh cricket with support vector machine. In: 2018 International Conference on Bangla Speech and Language Processing (ICBSLP), pp. 1–4. IEEE (2018)
12.
Rahman, A., Dey, E.K.: Datasets for aspect-based sentiment analysis in Bangla and its baseline evaluation. Data 3(2), 1–11 (2018)CrossRef
13.
Sarkar, K., Bhowmick, M.: Sentiment polarity detection in Bengali tweets using multinomial naïve bayes and support vector machines. In: 2017 IEEE Calcutta Conference (CALCON). pp. 31–36. IEEE (2017)
14.
Singla, Z., Randhawa, S., Jain, S.: Sentiment analysis of customer product reviews using machine learning. In: 2017 International Conference on Intelligent Computing and Control (I2C2), pp. 1–5. IEEE (2017)
15.
Srujan, K., Nikhil, S., Rao, H.R., Karthik, K., Harish, B., Kumar, H.K.: Classification of Amazon book reviews based on sentiment analysis. In: Information Systems Design and Intelligent Applications, pp. 401–411. Springer, Berlin (2018)
16.
Taher, S.A., Akhter, K.A., Hasan, K.A.: N-gram based sentiment mining for Bangla text using support vector machine. In: 2018 International Conference on Bangla Speech and Language Processing (ICBSLP), pp. 1–5. IEEE (2018)
17.
Wu, S., Jia, J.K.: Feature extraction based on improved feature weighting algorithm. In: Recent Trends in Decision Science and Management, pp. 703–708. Springer, Berlin (2020)
18.
Xu, G., Yu, Z., Yao, H., Li, F., Meng, Y., Wu, X.: Chinese text sentiment analysis based on extended sentiment dictionary. IEEE Access 7, 43749–43762 (2019)CrossRef
19.
Xu, J., Hu, Z., Zou, J., Bi, A.: Intelligent emotion detection method based on deep learning in medical and health data. IEEE Access 8, 3802–3811 (2020)CrossRef
20.
Zhou, G., Zhu, Z., Hu, X.T.: Cross-lingual sentiment classification with stacked autoencoders. Knowl. Info. Syst. 47(1), 27–44 (2016)CrossRef
Metadata
Title
Sentiment Polarity Detection on Bengali Book Reviews Using Multinomial Naïve Bayes
Authors
Eftekhar Hossain
Omar Sharif
Mohammed Moshiul Hoque
Copyright Year
2021
Publisher
Springer Singapore
Book
Progress in Advanced Computing and Intelligent Engineering

Print ISBN: 978-981-334-298-9

Electronic ISBN: 978-981-334-299-6

Copyright Year: 2021

DOI
https://doi.org/10.1007/978-981-33-4299-6_23