Title :
A review of techniques for sentiment analysis Of Twitter data
Author :
Bhuta, Sagar ; Doshi, Aayushi ; Doshi, Uehit ; Narvekar, Meera
Author_Institution :
Dwarkadas J. Sanghvi Coll. of Eng., Mumbai, India
Abstract :
There has been a rapid increase in the use of social networking websites in the last few years. People most conveniently express their views and opinions on a wide array of topics via such websites. Sentiment analysis of such data which comprises of people´s views is very important in order to gauge public opinion on a particular topic of interest. This paper reviews a number of techniques, both lexicon-based approaches as well as learning based methods that can be used for sentiment analysis of text. In order to adapt these techniques for sentiment analysis of data procured from one of the social networking websites, Twitter, a number of issues and challenges need to be addressed, which are put forward in this paper.
Keywords :
learning (artificial intelligence); maximum entropy methods; pattern classification; social networking (online); support vector machines; text analysis; Twitter data; learning based methods; lexicon-based approach; public opinion; sentiment analysis; social networking Web sites; text analysis; Niobium; Noise measurement; Sentiment Analysis; Social Networks; Supervised Learning;
Conference_Titel :
Issues and Challenges in Intelligent Computing Techniques (ICICT), 2014 International Conference on
Conference_Location :
Ghaziabad
DOI :
10.1109/ICICICT.2014.6781346