Optimizing N-Gram Based Text Feature Selection in Sentiment Analysis for Commercial Products in Twitter through Polarity Lexicons

被引：0
|
作者：
Cabanlit, Mark Anthony [1 ]
Espinosa, Kurt Junshean [1 ]
机构：
[1] Univ Philippines Cebu, Dept Comp Sci, Cebu, Philippines
来源：
5TH INTERNATIONAL CONFERENCE ON INFORMATION, INTELLIGENCE, SYSTEMS AND APPLICATIONS, IISA 2014 | 2014年
关键词：
Social Media; Twitter; Unigram; Bigram; Trigram; N-gram; Naive Bayes; Sentiment Analysis; Opinion Classification;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
This study aims to optimize N-gram based text feature selection in sentiment analysis for commercial products in twitter through polarity lexicons. This can be done by merging dictionary-based weighing with naive-Bayes classification of sentiments. The study is still ongoing but partial results show potential.
引用
收藏
页码：94 / +
页数：4
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