Apply word vectors for sentiment analysis of APP reviews

Vector representations for language have been shown to be useful in a number of Natural Language Processing tasks. In this paper, we aim to investigate the effectiveness of word vector representations for the problem of Sentiment Analysis. In particular, we target three sub-tasks namely sentiment wo...

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Bibliographic Details
Published in2016 3rd International Conference on Systems and Informatics (ICSAI) pp. 1062 - 1066
Main Authors Xian Fan, Xiaoge Li, Feihong Du, Xin Li, Mian Wei
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.11.2016
Subjects
Buildings
Computer science
Feature extraction
Mathematical model
Mobile communication
polarity of sentiment words detection
Sentiment analysis
sentiment words extraction
Training
vector representations
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Summary:Vector representations for language have been shown to be useful in a number of Natural Language Processing tasks. In this paper, we aim to investigate the effectiveness of word vector representations for the problem of Sentiment Analysis. In particular, we target three sub-tasks namely sentiment words extraction, polarity of sentiment words detection, and text sentiment prediction. We investigate the effectiveness of vector representations over different text data and evaluate the quality of domain-dependent vectors. Vector representations has been used to compute various vector-based features and conduct systematically experiments to demonstrate their effectiveness. Using simple vector based features, we achieve F1 85.77%, the recall 85.20%, and the accuracy 86.35% for text sentiment analysis of APP reviews.
DOI:10.1109/ICSAI.2016.7811108