NDLI: Enhanced Twitter Sentiment Analysis by Using Feature Selection and Combination

Content Provider	IEEE Xplore Digital Library
Author	Ang Yang Jun Zhang Lei Pan Yang Xiang
Copyright Year	2015
Description	Author affiliation: Sch. of Inf. Technol., Deakin Univ., Burwood, VIC, Australia (Ang Yang; Jun Zhang; Lei Pan; Yang Xiang)
Abstract	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.
Starting Page	52
Ending Page	57
File Size	200536
Page Count	6
File Format	PDF
ISBN	9781467384209
DOI	10.1109/SocialSec2015.9
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-11-16
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Training Sentiment analysis Machine learning algorithms Supervised learning Classification Supervized learning Feature extraction Twitter Performance
Content Type	Text
Resource Type	Article

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