Abstract
Twitter is a microblogging site in which users can post updates (tweets) to friends (followers). It has become an immense dataset of the so-called sentiments. In this paper, we introduce an approach to selection of a new feature set based on Information Gain, Bigram, Object-oriented extraction methods in sentiment analysis on social networking side. In addition, we also proposes a sentiment analysis model based on Naive Bayes and Support Vector Machine. Its purpose is to analyze sentiment more effectively. This model proved to be highly effective and accurate on the analysis of feelings.
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Le, B., Nguyen, H. (2015). Twitter Sentiment Analysis Using Machine Learning Techniques. In: Le Thi, H., Nguyen, N., Do, T. (eds) Advanced Computational Methods for Knowledge Engineering. Advances in Intelligent Systems and Computing, vol 358. Springer, Cham. https://doi.org/10.1007/978-3-319-17996-4_25
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DOI: https://doi.org/10.1007/978-3-319-17996-4_25
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-17995-7
Online ISBN: 978-3-319-17996-4
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