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Web Sentiment Analysis for Scoring Positive or Negative Words using Tweeter Data

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International Journal of Computer Applications
© 2014 by IJCA Journal
Volume 96 - Number 6
Year of Publication: 2014
Authors:
A. Pappu Rajan
S. P. Victor
10.5120/16801-6518

Pappu A Rajan and S P Victor. Article: Web Sentiment Analysis for Scoring Positive or Negative Words using Tweeter Data. International Journal of Computer Applications 96(6):33-37, June 2014. Full text available. BibTeX

@article{key:article,
	author = {A. Pappu Rajan and S. P. Victor},
	title = {Article: Web Sentiment Analysis for Scoring Positive or Negative Words using Tweeter Data},
	journal = {International Journal of Computer Applications},
	year = {2014},
	volume = {96},
	number = {6},
	pages = {33-37},
	month = {June},
	note = {Full text available}
}

Abstract

As people are free to say their opinions on anything using various social networking sites like Twitter, Facebook, Discussion forums, and blogs. Particularly Microblogging and text messaging have emerged and become dominated tool over the web. Twitter messages (tweets) is often used to share opinions and sentiments about the surrounding world. The availability of social content generated on sites such as Twitter creates new opportunities to study public opinion about the entity. This analysis we took twitter data for sentiment classification. The Sentiment analysis is done on a per-Tweet basis. The words in each Tweet are compared with those in other Tweets that have been previously labeled as "positive", or "negative". After looking at these words, the algorithm then judges whether the text in the Tweet is positive or negative based on the likelihood for each possibility. The overall objective of this paper is to determine the sentiment of the text, whether it is positive or negative, which is extended to strength of polarity also this approach is used to obtain the significant features and to analyzing the overall sentiment for each object by computing the weighted average for all the sentiments in the textual data.

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