Creat membership Creat membership
Sign in

Forgot password?

Confirm
  • Forgot password?
    Sign Up
  • Confirm
    Sign In
Creat membership Creat membership
Sign in

Forgot password?

Confirm
  • Forgot password?
    Sign Up
  • Confirm
    Sign In
Collection
For ¥0.57 per day, unlimited downloads CREATE MEMBERSHIP Download

toTop

If you have any feedback, Please follow the official account to submit feedback.

Turn on your phone and scan

home > search >

TOM: Twitter opinion mining framework using hybrid classification scheme

Author:
Khan, Farhan Hassan   Bashir, Saba   Qamar, Usman  


Journal:
Decision Support Systems


Issue Date:
2014


Abstract(summary):

Twitter has become one of the most popular micro-blogging platform recently. Millions of users can share their thoughts and opinions about different aspects and events on the micro-blogging platform. Therefore, Twitter is considered as a rich source of information for decision making and sentiment analysis. Sentiment analysis refers to a classification problem where the main focus is to predict the polarity of words and then classify them into positive and negative feelings with the aim of identifying attitude and opinions that are expressed in any form or language. Sentiment analysis over Twitter offers organisations a fast and effective way to monitor the publics' feelings towards their brand, business, directors, etc. A wide range of features and methods for training sentiment classifiers for Twitter datasets have been researched in recent years with varying results. The primary issues in previous techniques are classification accuracy, data sparsity and sarcasm, as they incorrectly classify most of the tweets with a very high percentage of tweets incorrectly classified as neutral. This research paper focuses on these problems and presents an algorithm for twitter feeds classification based on a hybrid approach. The proposed method includes various pre-processing steps before feeding the text to the classifier. Experimental results show that the proposed technique overcomes the previous limitations and achieves higher accuracy when compared to similar techniques. (C) 2013 Elsevier B.V. All rights reserved.


Page:
245-257


VIEW PDF

The preview is over

If you wish to continue, please create your membership or download this.

Create Membership

Similar Literature

Submit Feedback

This function is a member function, members do not limit the number of downloads