A Comprehensive Study on Lexicon Based Approaches for Sentiment AnalysisAuthor : Venkateswarlu Bonta, Nandhini Kumaresh and N. Janardhan
Volume 8 No.2 Special Issue:March 2019 pp 1-6
In recent years, it is seen that the opinion-based postings in social media are helping to reshape business and public sentiments, and emotions have an impact on our social and political systems. Opinions are central to mostly all human activities as they are the key influencers of our behaviour. Whenever we need to make a decision, we generally want to know others opinion. Every organization and business always wants to find customer or public opinion about their products and services. Thus, it is necessary to grab and study the opinions on the Web. However, finding and monitoring sites on the web and distilling the reviews remains a big task because each site typically contains a huge volume of opinion text and the average human reader will have difficulty in identifying the polarity of each review and summarizing the opinions in them. Hence, it needs the automated sentiment analysis to find the polarity score and classify the reviews as positive or negative. This article uses NLTK, Text blob and VADER Sentiment analysis tool to classify the movie reviews which are downloaded from the website www.rottentomatoes.
com that is provided by the Cornell University, and makes a comparison on these tools to find the efficient one for sentiment classification. The experimental results of this work confirm that VADER outperforms the Text blob.
Sentiment Analysis, Opinion Mining, Sentiwordnet, NLTK, Text blob, VADER
 Vikas Malik and Amit Kumar. “Sentiment Analysis of Twitter Data Using Naive Bayes Algorithm”, International Journal on Recent and Innovation Trends in Computing and Communication, Vol. 6, No. 4, 2018.
 J.Ge, M.Alonso Vazquez, and U.Gretzel,“Sentiment analysis: a review”, In Sigala, M. &Gretzel, U. (Eds.), Advances in Social Media for Travel, Tourism and Hospitality: New Perspectives, Practice and Cases,pp. 243-261. New York: Routledge, 2018.
 Z. Nanli, Z. Ping, L. Weiguo, and C. Meng, “Sentiment analysis: A literature review”, Proceedings of the International Symposium on Management of Technology (ISMOT), Hangzhou, IEEE, pp. 572-576, 2012.
 J.W. Pennebaker, R.L. Boyd, K. Jordan, and K. Blackburn, “The development and psychometric properties of LIWC2015”, Austin, TX: University of Texas at Austin, 2015
 S. Baccianella, A. Esuli, and F. Sebastiani, “SENTIWORDNET 3.0 : An Enhanced Lexical Resource for Sentiment Analysis and Opinion Mining”, pp. 2200–2204, 2008
 M. Hu and B. Liu, “Opinion Extraction and Summarization on the Web”, pp. 1621–1624.
 B. Pang, L. Lee, H. Rd, and S. Jose, “Thumbs up ? Sentiment Classification using Machine Learning Techniques”, pp. 79–86, 2002
 Wordnet.com, “WordNet, a Lexical database for English”, [online] Available: http://wordnet. princeton.edu/
 Textbolb.com, “Textblob Tutorial, Quickstart“, [online] Available at: https://textblob.readthedocs. io/en/latest/quickstart.html#quickstart
 C. J. Hutto and E. Gilbert, “VADER : A Parsimonious Rule-based Model for Sentiment Analysis of Social Media Text”, Proceedings of the Eighth International AAAI Conference on Weblogs and Social Media, , pp. 216–225, 2014
 Cornell.edu, “Movie Review data”, [online] Available: http://www.cs.cornell.edu/people/pabo/movie-review-data/
 Steven Bird and Edward Loper. “NLTK: The Natural Language Toolkit”, 2006
 Bing Liu,“Sentiment Analysis and Opinion Mining”, Morgan &Claypool Publishers, May 2012.
 Adamo and David. “A Text Similarity Approach to Sentiment Classification (of Movie Reviews) using SentiWordNet” .10.13140/RG.2.1.3271.1120, 2015
 H. Han, Y. Zhang, J. Zhang, J. Yang, and X. Zou, “Improving the performance of lexicon-based review sentiment analysis method by reducing additional introduced sentiment bias”,, pp. 1–11, 2018
 Steven Loria. “Textblob Documentation”,Release 0.15.2,2018