Text classification by labeling words
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Automatically Determining Attitude Type and Force for Sentiment Analysis
Human Language Technology. Challenges of the Information Society
Using emoticons to reduce dependency in machine learning techniques for sentiment classification
ACLstudent '05 Proceedings of the ACL Student Research Workshop
Sentiment knowledge discovery in twitter streaming data
DS'10 Proceedings of the 13th international conference on Discovery science
Measuring message propagation and social influence on Twitter.com
SocInfo'10 Proceedings of the Second international conference on Social informatics
Robust sentiment detection on Twitter from biased and noisy data
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Posters
MOA-TweetReader: real-time analysis in Twitter streaming data
DS'11 Proceedings of the 14th international conference on Discovery science
Emotion tokens: bridging the gap among multilingual twitter sentiment analysis
AIRS'11 Proceedings of the 7th Asia conference on Information Retrieval Technology
Aspect and sentiment extraction based on information-theoretic co-clustering
ISNN'12 Proceedings of the 9th international conference on Advances in Neural Networks - Volume Part II
Random walk weighting over sentiwordnet for sentiment polarity detection on Twitter
WASSA '12 Proceedings of the 3rd Workshop in Computational Approaches to Subjectivity and Sentiment Analysis
Hi-index | 0.00 |
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.