A Sentiment Computing for the Opinions Based on the Twitter

Authors(4) :-Pooja Dhamanekar, Pooja Bindage, Chetan Arage, Mahesh Gaikwad

The era of social networking has increased the amount of data generated by the user. People from all over the world share their opinions and thoughts on the micro-blogging sites on daily basis. As the use of internet such as websites, social networks, and blogs increases online portals reviews, opinions, recommendations, ratings, and feedbacks are also generated by users. Twitter is one of the most widely used micro-blogging site where people share their reviews in the form of tweets. This user can give their opinion on anything like books, people, hotels, products, research, events, etc. These sentiments become very useful for businesses, governments, and individuals. However, there are several challenges facing the sentiment analysis and evaluation process. These challenges become mountain in analyzing the accurate meaning of sentiments and measuring sentiment polarity. Therefore, we propose an innovative method to do the sentiment computing for opinions. Our method is based on the social media data of a Tweets, a Word Emotion Association Network (WEAN) is built to jointly express its semantics and emotions, which lays the foundation for the opinion sentiment computation.

Authors and Affiliations

Pooja Dhamanekar
Professor, Department of Computer Science and Engineering, Sanjay Ghodawat Institute Atigre, Kolhapur, Maharashtra, India
Pooja Bindage
Professor, Department of Computer Science and Engineering, Sanjay Ghodawat Institute Atigre, Kolhapur, Maharashtra, India
Chetan Arage

Mahesh Gaikwad

Sentiment computing, Emotion classiļ¬cation, Social media big data, Opinions, Text mining.

  1. B. Pang, L. Lee, and S. Vaithyanathan, "Thumbs up?: sentiment classi?cation using machine learning techniques," in Proceedings of the ACL-02 conference on Empirical methods in natural language processing-Volume 10. Association for Computational Linguistics, 2002, pp. 79–86.
  2. S.-M. Kim and E. Hovy, "Automatic identi?cation of pro and con reasons in online reviews," in Proceedings of the COLING/ACL on Main conference poster sessions. Association for Computational Linguistics, 2006, pp. 483–490.
  3. M. Taboada, J. Brooke, M. To?loski, K. Voll, and M. Stede, "Lexicon-based methods for sentiment analysis," Computational linguistics, vol. 37, no. 2, pp. 267–307, 2011
  4. D. G. A.-K. D. Matthias Steinbauer, Dr Maria Indrawan-Santiago, Y. Yamamoto, T. Kumamoto, and A. Nadamoto, "Multidimensional sentiment calculation method for twitter based on emoticons," International Journal of Pervasive Computing and Communications, vol. 11, no. 2, pp. 212–232, 2015.
  5. D. Tang, B. Qin, and T. Liu, "Learning semantic representations of users and products for document level sentiment classi?cation," in Meeting of the Association for Computational Linguistics and the International Joint Conference on Natural Language Processing, 2015.
  6. J. Read, "Using emoticons to reduce dependency in machine learning techniques for sentiment classi?cation," in Proceedings of the ACL student research workshop. Association for Computational Linguistics, 2005, pp. 43–48.
  7. A. Go, R. Bhayani, and L. Huang, "Twitter sentiment classi?cation using distant supervision," CS224N Project Report, Stanford, vol. 1, p. 12, 2009.
  8. N. Jindal and B. Liu, "Identifying Comparative Sentences in Text Documents", In Proceedings of SIGIR’06, (2006) , pp.244-251.
  9. C.C. Chang, C.J.Lin, "Libsvm: a library for support vector machines", Trans IntellSystTechnol, vol. 2, no. 3, (2011), pp.27
  10. R. Song, H. F. Lin, and F. Chang, "Chinese Comparative Sentences Identification and Comparative Relations Extraction", Journal of Chinese Information Processing, vol. 23, no. 2, (2009), pp.102-107.
  11. Dandan Jiang, Xiangfeng Luo∗, Member, IEEE, Junyu Xuan, Zheng Xu, "Sentiment Computing for the News Event Based on the Social Media Big Data" DOI 10.1109/ACCESS.2016.2607218

Publication Details

Published in : Volume 3 | Issue 3 | March-April 2018
Date of Publication : 2018-04-30
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 899-904
Manuscript Number : CSEIT1833227
Publisher : Technoscience Academy

ISSN : 2456-3307

Cite This Article :

Pooja Dhamanekar, Pooja Bindage, Chetan Arage, Mahesh Gaikwad, "A Sentiment Computing for the Opinions Based on the Twitter ", International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN : 2456-3307, Volume 3, Issue 3, pp.899-904, March-April-2018.
Journal URL : http://ijsrcseit.com/CSEIT1833227

Follow Us

Contact Us