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Sentiment Analysis of Twitter Data: A Survey of Techniques

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2016
Vishal A. Kharde, S.S. Sonawane

Vishal A Kharde and S S Sonawane. Article: Sentiment Analysis of Twitter Data: A Survey of Techniques. International Journal of Computer Applications 139(11):5-15, April 2016. Published by Foundation of Computer Science (FCS), NY, USA. BibTeX

	author = {Vishal A. Kharde and S.S. Sonawane},
	title = {Article: Sentiment Analysis of Twitter Data: A Survey of Techniques},
	journal = {International Journal of Computer Applications},
	year = {2016},
	volume = {139},
	number = {11},
	pages = {5-15},
	month = {April},
	note = {Published by Foundation of Computer Science (FCS), NY, USA}


With the advancement of web technology and its growth, there is a huge volume of data present in the web for internet users and a lot of data is generated too. Internet has become a platform for online learning, exchanging ideas and sharing opinions. Social networking sites like Twitter, Facebook, Google+ are rapidly gaining popularity as they allow people to share and express their views about topics, have discussion with different communities, or post messages across the world. There has been lot of work in the field of sentiment analysis of twitter data. This survey focuses mainly on sentiment analysis of twitter data which is helpful to analyze the information in the tweets where opinions are highly unstructured, heterogeneous and are either positive or negative, or neutral in some cases. In this paper, we provide a survey and a comparative analyses of existing techniques for opinion mining like machine learning and lexicon-based approaches, together with evaluation metrics. Using various machine learning algorithms like Naive Bayes, Max Entropy, and Support Vector Machine, we provide research on twitter data streams.We have also discussed general challenges and applications of Sentiment Analysis on Twitter.


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Twitter, Sentiment analysis (SA), Opinion mining, Machine learning, Naive Bayes (NB), Maximum Entropy, Support Vector Machine (SVM).