Tweet Sentiment Analysis with Latent Dirichlet Allocation

Masahiro Ohmura, Koh Kakusho and Takeshi Okadome
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Masahiro Ohmura: Kwansei Gakuin University, Sanda-shi, Japan
Koh Kakusho: Kwansei Gakuin University, Sanda-shi, Japan
Takeshi Okadome: Kwansei Gakuin University, Sanda-shi, Japan

International Journal of Information Retrieval Research (IJIRR), 2014, vol. 4, issue 3, 66-79

Abstract: 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.

Date: 2014
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