A Machine Learning Based Approach for Opinion Mining on Social Network Data

Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 5)

Abstract

Micro-blogging has been widely used for voicing out opinions in the public domain. One such website, Twitter is a point of attraction for researchers in the areas such as prediction of electoral events, movie box office, stock market, consumer brands etc. In our paper, we focus on using Twitter, for the task of opinion mining. We explore how combining the different parameters affect the accuracy of the machine-learning algorithms with respect to the consumer products. In this paper, we have combined the methods of feature extraction with a parameter known as negation handling. Negation words can awfully change the meaning of a sentence and hence the sentiment expressed in them. We experimented with supervised learning methods like Naïve Bayes (NB) Classifier and Maximum Entropy (MaxEnt) Classifier along with optimization iteration algorithms i.e., Generalized Iterative Scaling (GIS) and Improved Iterative Scaling (IIS). Experimental evaluations show that our proposed technique is better. We have obtained a 99.29% of specificity measure using the MaxEnt-IIS Classifier.

Keywords

Opinion mining Sentiment analysis Negation detection Supervised learning 

References

  1. 1.
    Marko Skoric, Nathaniel Poor, Palakorn Achananuparp, Ee-Peng Lim, Jing Jiang et al. “Tweets and Votes: A Study of the 2011 Singapore General Election” In proceedings at 2012 45th Hawaii International Conference on System Sciences., (2012)Google Scholar
  2. 2.
    Malhar Anjaria, Ram Mahana Reddy Guddeti, “Influence Factor Based Opinion Mining of Twitter Data Using Supervised Learning”, Proceeding of the Sixth International Conference on Communication System and Networks, (2014)Google Scholar
  3. 3.
    Alexander Pak and Patrick Paroubek. “Twitter as a corpus for sentiment analysis and opinion mining”, Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC’ 10), (2010)Google Scholar
  4. 4.
    Bo Pang. Lilliam Lee, “Seeing Stars: Exploiting class relationships for sentiment categorization with respect to rating scales”, (2002)Google Scholar
  5. 5.
    Grigori Sidorov, Juan Gordon et al., Empirical Study of Machine Learning Based Approach for Opinion Mining in Tweets. In 11th Mexican International Conference on Artificial Intelligence, MICAI 2012, San Luis Potosí, Mexico, (2012)Google Scholar
  6. 6.
    Walaa Medhat, Ahmed Hassan, Hoda korashy, Sentiment analysis algorithms and applications: A survey, In Ain Shams Engineering Journal, www.sciencedirect.com, (2014)
  7. 7.
    Antonie Boutet et al., What’s in your tweet: I know Who You Supported in the UK 2010 general elections, Association for the Advancement of Artificial Intelligence, (2012)Google Scholar
  8. 8.
    Sanjiv Das and Mike Chen. Yahoo! for Amazon: Extracting market sentiment rom stock message boards. In Proceedings of the Asia Pacific Finance Association Annual Conference (APFA), (2001)Google Scholar
  9. 9.
    Meeyoung, C. et al., Measuring User Influence in Twitter: The Million Follower Fallacy. In Fourth International AAAI Conference on Weblogs and Social Media, (2010)Google Scholar
  10. 10.
    Johan Bollen, Alberto Pepe, and Huina Mao. “Modeling public mood and emotion: Twitter sentiment and socioeconomic phenomena”. In Proceedings of the Fifth International AAAI Conference on Weblogs and Social Media (ICWSM), Barcelona, Spain, (2011)Google Scholar
  11. 11.
    Dadvar, Maral and Hauff, Claudia and Jong, Franciska de, Scope of negation detection in sentiment analysis. In Dutch-Belgian Information Retrieval Workshop, Amsterdam, the Netherlands (pp 16–20) (2011).Google Scholar
  12. 12.
    Luciano Barbosa and Junlan Feng. 2010. Robust sentiment detection on Twitter from biased and noisy data. In Proc. of CO LING, (2010)Google Scholar
  13. 13.
    Amna Asmi and Tanko Ishaya. Negation Identification and Calculation in Sentiment Analysis, In 2nd International Conference on Advances in Information Mining and Management. IMMM, (2012).Google Scholar
  14. 14.
    Alec Go et al, Twitter sentiment classification using distant supervision, Stanford University, (2009)Google Scholar
  15. 15.
    Kamal Nigam, John Lafferty, Andrew McCallum, Using Maximum Entropy for Text Classification, In IJCAI-99 Workshop on Machine Learning for Information Filtering, pages 61–67, (1999)Google Scholar
  16. 16.
    Brendon O’Connor and Balasubramanyan et al,From Tweets to Polls: Linking Text Sentiment to Public Opinion Time Series, Proceedings of the International AAAI Conference on Weblogs and Social Media, Washington, DC, (2010)Google Scholar
  17. 17.
    Isaac G Councill, Ryan McDonald, and Leonid Velikovich. What’s great and what’s not: learning to classify the scope of negation for improved sentiment analysis. In Proceedings of the workshop on negation and speculation in natural language processing, pages 51–59. Association for Computational Linguistics, (2010)Google Scholar
  18. 18.
    Pang and Lee, 2002, Sentiment Classification using Machine Learning Techniques, Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), Philadelphia, (2002)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2017

Authors and Affiliations

  1. 1.Muffakham Jah College of Engineering and TechnologyHyderabadIndia

Personalised recommendations