Tweet Sentiment Analysis with Latent Dirichlet Allocation

The method proposed here analyzes the social sentiments from collected tweets that have at least 1 of 800 sentimental or emotional adjectives. By dealing with tweets posted in a half a day as an input document, the method uses Latent Dirichlet Allocation (LDA) to extract social sentiments, some of w...

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Bibliographic Details
Published inInternational journal of information retrieval research Vol. 4; no. 3; pp. 66 - 79
Main Authors Ohmura, Masahiro, Kakusho, Koh, Okadome, Takeshi
Format Journal Article
LanguageEnglish
Published Hershey IGI Global 01.07.2014
Subjects
Autoregressive moving-average models
Data mining
Dirichlet problem
Regression models
Sensitivity
Sentiment analysis
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Summary:The method proposed here analyzes the social sentiments from collected tweets that have at least 1 of 800 sentimental or emotional adjectives. By dealing with tweets posted in a half a day as an input document, the method uses Latent Dirichlet Allocation (LDA) to extract social sentiments, some of which coincide with our daily sentiments. The extracted sentiments, however, indicate lowered sensitivity to changes in time, which suggests that they are not suitable for predicting daily social or economic events. Using LDA for the representative 72 adjectives to which each of the 800 adjectives maps while preserving word frequencies permits us to obtain social sentiments that show improved sensitivity to changes in time. A regression model with autocorrelated errors in which the inputs are social sentiments obtained by analyzing the contracted adjectives predicts Dow Jones Industrial Average (DJIA) more precisely than autoregressive moving-average models.
ISSN:2155-6377
2155-6385
DOI:10.4018/IJIRR.2014070105