Sentiment Analysis Using N-gram Technique
Dramatic growth of social media has created remarkable interest among Internet users nowadays. Information from these Web sites in the form of reviews, feedbacks, ratings, etc., can be utilized for various purposes like to find out users’ taste or interest to develop a proper marketing strategy, maybe for a survey about the product by using sentiment analysis. Twitter is generally used for posting long comments in short status. Twitter offers organizations a fast and powerful approach to investigate customers’ viewpoints toward the critical to success in the open market. Previously we calculate sentiment of each word for the sentiment, which may or may not be accurate because may be the same word used in past for negative review, but presently it is used for positive sense. We propose a method by applying both log function and N-gram techniques to find out the sentiment of the Twitter data in R to build a robust engine to achieve more accuracy.
KeywordsSentiment analysis Preprocessing N-grams
We are thankful to the faculty members of School of Computer Engineering Department of KIIT University, Bhubaneswar, for their cooperation and suggestions.
- 2.Himadri Tanaya Chidananda, Santwana Sagnika and Laxman Sahoo. Survey on Sentiment Analysis: A Comparative Study. International Journal of Computer Applications 159(6): 4–7, February 2017.Google Scholar
- 3.Anto, Menara P., Kerala Thrissur, Mejo Antony, KM Muhsina, Nivea Johny, Vinay James, and Aswathy Wilson. “Product Rating Using Sentiment Analysis” International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT), 2016.Google Scholar
- 4.Niu, Zhen, Zelong Yin, and Xiangyu Kong. “Sentiment classification for microblog by machine learning.” Computational and Information Sciences (ICCIS), 2012 Fourth International Conference on. Ieee, 2012.Google Scholar
- 5.Domingos, Pedro, and Michael Pazzani. “On the optimality of the simple Bayesian classifier under zero-one loss.” Machine learning 29.2 (1997): 103–130.Google Scholar
- 6.Pak, Alexander, and Patrick Paroubek. “Twitter as a Corpus for Sentiment Analysis and Opinion Mining.” LREc. Vol. 10. No. 2010. 2010.Google Scholar
- 7.Sarlan, Aliza, Chayanit Nadam, and Shuib Basri. “Twitter sentiment analysis.” Information Technology and Multimedia (ICIMU), 2014 International Conference on. IEEE, 2014.Google Scholar
- 8.Jianqiang, Zhao. “Pre-processing Boosting Twitter Sentiment Analysis?” Smart City/SocialCom/SustainCom (SmartCity), 2015 IEEE International Conference on. IEEE, 2015.Google Scholar
- 9.Kumar, Monu, and Anju Bala. “Analyzing Twitter sentiments through big data.” Computing for Sustainable Global Development (INDIACom), 2016 3rd International Conference on. IEEE, 2016.Google Scholar
- 10.Zhao, Jianqiang, and Xiaolin Gui. “Comparison Research on Text Pre-processing Methods on Twitter Sentiment Analysis.” IEEE Access (2017).Google Scholar
- 11.Kuat Yessenov, Sasa Misailovic, “Sentiment Analysis of Movie Review Comments”, 6.863 Spring 2009 final project, pp. 1–17.Google Scholar
- 12.Web link-https://en.wikipedia.org/wiki/N-gram.