Enhanced Twitter sentiment analysis by using feature selection and combination
Yang, Ang, Zhang, Jun, Pan, Lei and Xiang, Yang 2015, Enhanced Twitter sentiment analysis by using feature selection and combination, in SOCIALSEC 2015: Proceedings of the International Symposium on Security and Privacy in Social Networks and Big Data, IEEE, Piscataway, N.J., pp. 52-57, doi: 10.1109/SocialSec2015.9.
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Enhanced Twitter sentiment analysis by using feature selection and combination
Tweet sentiment analysis is an important research topic. An accurate and timely analysis report could give good indications on the general public's opinions. After reviewing the current research, we identify the need of effective and efficient methods to conduct tweet sentiment analysis. This paper aims to achieve a high level of performance for classifying tweets with sentiment information. We propose a feasible solution which improves the level of accuracy with good time efficiency. Specifically, we develop a novel feature combination scheme which utilizes the sentiment lexicons and the extracted tweet unigrams of high information gain. We evaluate the performance of six popular machine learning classifiers among which the Naive Bayes Multinomial (NBM) classifier achieves the accuracy rate of 84.60% and takes a few minutes to complete classifying thousands of tweets.
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