A Novel Adaptable Approach for Sentiment Analysis

Authors(4) :-Aishwarya R, Ashwatha C, Deepthi A, Beschi Raja J

The internet has provided many novel ways for people to express their ideas and views about different topics, ideas and trends. The contents generated by the users which are present on various mediums like internet blogs, discussion forums, and groups paves a strong base for decision making in diverse fields such as digital advertising, election polls, scientific predictions, market surveys and business zones etc. Sentiment analysis is the process of mining the sentiments from the data that are available in online platforms and categorizing the opinion towards a particular entity that falls on three different categories which are positive, neutral and negative. In this paper, the problem of sentiment classification of election dataset in twitter has been addressed. This paper summarizes the ensemble method, the best way to achieve classification. And also about the ada boosting algorithm and artificial neural networks by which the optimized prediction accuracy is achieved.

Authors and Affiliations

Aishwarya R
Department of Computer Science and Engineering, Sri Krishna College of Technology, Coimbatore, Tamil Nadu, India
Ashwatha C
Department of Computer Science and Engineering, Sri Krishna College of Technology, Coimbatore, Tamil Nadu, India
Deepthi A
Department of Computer Science and Engineering, Sri Krishna College of Technology, Coimbatore, Tamil Nadu, India
Beschi Raja J
Department of Computer Science and Engineering, Sri Krishna College of Technology, Coimbatore, Tamil Nadu, India

Sentiment Analysis, Twitter, Opinion Mining, Social Media.

  1. R. Addo-tenkorang and P. T. Helo, ‘‘Big data applications in operations/supply-chain management: A literature review,’’ Comput. Ind. Eng., vol. 101, pp. 528–543, Nov. 2016
  2. R. Piryani, D. Madhavi, and V. K. Singh, ‘‘Analytical mapping of opinion mining and sentiment analysis research during 2000–2015,’’ Inf. Process. Manage., vol. 53, no. 1, pp. 122–150, 2017.
  3. A. Pak,and P. Paroubek, “Twitter as a Corpus for Sentiment Analysis and Opinion Mining,” Special Issue of International Journal of Computer Application, France:University Paris-Sud, 2010.
  4. “Three Cool and Inexpensive Tools to Track Twitter Hashtags”, June 11,2013.[Online].Available [Accessed: 19-Oct-2015].
  5. K. Ghag and K. Shah, “Comparative analysis of the techniques for Sentiment Analysis”, in Int. Conf. on Advances in Technology and Engineering, 2013,.
  6. K. Khan, B. Baharudin, A. Khan and F. Malik, “Mining Opinion from Text Documents: A Survey”, Digital Ecosystems and Technologies, 2009.
  7. B. Pang, and L. Lee, “Opinion mining and sentiment analysis,” 2nd workshop on making sense of Microposts. Ithaca: Cornell University. Vol.2(1), 2008.
  8. E. Kouloumpis, T. Wilson, and J. Moore, “Twitter Sentiment Analysis:The Good the Bad and theOMG!”, (Vol.5). International AAAI, 2011.
  9. A. Sarlan, C. Nadam and S. Basri, “Twitter Sentiment Analysis”, in Int. Conf. on Information Technology and Multimedia, 2014.
  10. Cui A, Zhang M, Liu Y, Ma S, 2011. Emotion tokens: Bridging the gap among multilingual twitter sentiment analysis. In: Asia Information Retrieval Symposium. Springer
  11. M.Taboada, J. Brooke, M. Tofiloski, K. Voll, and M. Stede, “ Lexicon Based Methods for Sentiment Analysis,” Association for Computational Linguistics, 2011
  12. Tang J, Hu X, Gao H, Liu H, 2013. Exploiting local and global social context for recommendation. In: Ijcai. pp. 2712–2718.
  13. E. Cambria, ‘‘Affective computing and sentiment analysis,’’ IEEE Intell. Syst., vol. 31, no. 2, pp. 102–107, Mar./Apr. 2016.
  14. D. H. Wolpert and W. G. Macready, ‘‘No free lunch theorems for search,’’ Santa Fe Institute, Santa Fe, NM, USA, Tech. Rep. SFI-TR-05- 010, 1995.
  15. Rosenthal, Sara, NouraFarra, and PreslavNakov. "SemEval-2017 task 4: Sentiment analysis in Twitter." In Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017), pp. 502-518. 2017.
  16. Medhat, Walaa, Ahmed Hassan, and HodaKorashy. "Sentiment analysis algorithms and applications: A survey." Ain Shams Engineering Journal 5, no. 4 (2014)
  17. Z. Kechaou, B. M. Ammar and A. M. Alimi, “Improving e-learning with sentiment analysis of users' opinions”, in Global Engineering Education Conference (EDUCON), 2011
  18. E. H.-J. Kim, Y. K. Jeong, Y. Kim, K. Y. Kang, and M. Song, ‘‘Topic-based content and sentiment analysis of Ebola virus on Twitter and in the news,’’ J. Inf. Sci., vol. 42, no. 6, pp. 763–781, 2016.
  19. H. Saif, Y. He and H. Alani, “Alleviating Data Scarcity for Twitter Sentiment Analysis”. Association for Computational Linguistics, 2012.
  20.  R. Dong, M. P. O’Mahony, M. Schaal, K. McCarthy, and B. Smyth, ‘‘Combining similarity and sentiment in opinion mining for product recommendation,’’ J. Intell. Inf. Syst., vol. 46, no. 2, pp. 285–312, 2016
  21. E. D. Avanzo and G. Pilato, ‘‘Mining social network users opinions ‘to aid buyers’ shopping decisions,’’ Comput. Hum. Behav., vol. 51, pp. 1284–1294, Oct. 2015.
  22. E. Ferrara, O. Varol, C. Davis, F. Menczer, and A. Flammini, ‘‘The rise of social bots,’’ Commun. ACM, vol. 59, no. 7, pp. 96–104, 2016.
  23. X. Zhang, H. Fuehres, and P. A. Gloor, ‘‘Predicting stock market indicators through Twitter ‘I hope it is not as bad as I fear,’’’ Procedia-Social Behav. Sci., vol. 26, pp. 55–62, Jan. 2011.
  24. N. Li and D. D. Wu, ‘‘Using text mining and sentiment analysis for online forums hotspot detection and forecast,’’ Decis. Support Syst., vol. 48, no. 2, pp. 354–368, 2010
  25. A. Agarwal, B. Xie, I. Vovsha, O. Rambow, and R.Passonneau, “Sentiment Analysis of Twitter Data,” Annual International Conferences. New York:Columbia University, 2012.
  26. S. Pei, L. Muchnik, J. S. Andrade, Jr., Z. Zheng, and H. A. Makse, ‘‘Searching for superspreaders of information in real-world social media,’’ Sci. Rep., vol. 4, Jul. 2014, Art. no. 5547.
  27. A.C.E.S Lima. and L.N. de Castro, “Automatic sentiment analysis of Twitter messages”, in 4 th Int. Conf. on Computational Aspects of Social Networks (CASoN), 2012
  28. Wasserman S, Faust K, 1994. Social network analysis: Methods and applications. Vol. 8. Cambridge university press
  29. V. Sehgal and C. Song, “SOPS: Stock Prediction Using Web Sentiment”, in 7th IEEE Int. Conf. on Data Mining Workshop, 2007
  30. P. Nakov, Z. Kozareva, A. Ritter, S. Rosenthal, V. Stoyanov, T. Wilson, Sem Eval-2013 Task2:Sentiment AnalysisinTwitter (Vol.2,pp. 312-320 ,2013.
  31. Tan C, Lee L, Tang J, Jiang L, Zhou M, Li P, 2011. User-level sentiment analysis incorporating social networks. In: Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM.
  32. Thelwall Mike. 2010. Emotion Homophily in Social Network Site Messages. First Monday 15(4)
  33. A. Pak,and P. Paroubek, “Twitter as a Corpus for Sentiment Analysis and Opinion Mining,” Special Issue of International Journal of Computer Application, France:Universitede Paris-Sud, 2010.
  34. S. Bahrainian and A. Dangel, “Sentiment Analysis using Sentiment Features”, in Int. joint Conf. of Web Intelligence and Intelligent Agent Technologies, 2013.
  35. Mei Q, Ling X, Wondra M, Su H, Zhai C, 2007. Topic sentiment mixture: modeling facets and opinions in weblogs. In: Proceedings of the 16th international conference on World Wide Web. ACM
  36.  Pang, Bo, and Lillian Lee. "Opinion mining and sentiment analysis." Foundations and Trends® in Information Retrieval 2, no. 1–2 (2008)
  37. N. Altrabsheh, M. Cocea and S. Fallahkhair, “Sentiment analysis: towards a tool for analysing real-time students feedback”, in 26th International Conference on Tools with Artificial Intelligence, 2014.
  38. A. M. Azmi and S. M. Alzanin, ‘‘‘Aara’—A system for mining the polarity of Saudi public opinion through e-newspaper comments,’’ J. Inf. Sci., vol. 40, no. 3, pp. 398–410, 2014
  39. P. Sobkowicz, M. Kaschesky, and G. Bouchard, ‘‘Opinion mining in social media: Modeling, simulating, and forecasting political opinions in the Web,’’ Government Inf. Quart., vol. 29, no. 4, pp. 470–479, 2012
  40. B. Gokulakrishnan, P. Plavnathan, R. Thiruchittampalam, A. Perera and N. Prasath, “Opinion Mining and Sentiment Analysis on a Twitter Data Stream”, in Int. Conf. on Advances in ICT for Engineering Regions, 2012, pp. 182-188.
  41. C. Chiu, N.-H. Chiu, R.-J. Sung, and P.-Y. Hsieh, ‘‘Opinion mining of hotel customer-generated contents in Chinese weblogs,’’ Current Issues Tourism, vol. 18, no. 5, pp. 477–495, 2015.
  42. A. M. Azmi and S. M. Alzanin, ‘‘‘Aara’—A system for mining the polarity of Saudi public opinion through e-newspaper comments,’’ J. Inf. Sci., vol. 40, no. 3, pp. 398–410, 2014.
  43. Tan C, Lee L, Tang J, Jiang L, Zhou M, Li P, 2011. User-level sentiment analysis incorporating social networks. In: Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, pp. 1397–1405
  44.  M. del Pilar Salas-Zàrate, E. López-López, R. Valencia-García, N. Aussenac-Gilles, Á. Almela, and G. Alor-Hernández, ‘‘A study on LIWC categories for opinion mining in Spanish reviews,’’ J. Inf. Sci., vol. 40, no. 6, pp. 749–760, 2014.
  45. K. Pasupa, P. Netisopakul, and R. Lertsuksakda, ‘‘Sentiment analysis of Thai children stories,’’ Artif. Life Robot., vol. 21, no. 3, pp. 357–364, 2016.

Publication Details

Published in : Volume 5 | Issue 2 | March-April 2019
Date of Publication : 2019-04-30
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 254-263
Manuscript Number : CSEIT195263
Publisher : Technoscience Academy

ISSN : 2456-3307

Cite This Article :

Aishwarya R, Ashwatha C, Deepthi A, Beschi Raja J, "A Novel Adaptable Approach for Sentiment Analysis", International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN : 2456-3307, Volume 5, Issue 2, pp.254-263, March-April-2019. Available at doi : https://doi.org/10.32628/CSEIT195263
Journal URL : http://ijsrcseit.com/CSEIT195263

Article Preview