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Tweet Sentiment Analysis with Latent Dirichlet Allocation

Tweet Sentiment Analysis with Latent Dirichlet Allocation

Masahiro Ohmura, Koh Kakusho, Takeshi Okadome
Copyright: © 2014 |Volume: 4 |Issue: 3 |Pages: 14
ISSN: 2155-6377|EISSN: 2155-6385|EISBN13: 9781466654891|DOI: 10.4018/IJIRR.2014070105
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MLA

Ohmura, Masahiro, et al. "Tweet Sentiment Analysis with Latent Dirichlet Allocation." IJIRR vol.4, no.3 2014: pp.66-79. https://doi.org/10.4018/IJIRR.2014070105

APA

Ohmura, M., Kakusho, K., & Okadome, T. (2014). Tweet Sentiment Analysis with Latent Dirichlet Allocation. International Journal of Information Retrieval Research (IJIRR), 4(3), 66-79. https://doi.org/10.4018/IJIRR.2014070105

Chicago

Ohmura, Masahiro, Koh Kakusho, and Takeshi Okadome. "Tweet Sentiment Analysis with Latent Dirichlet Allocation," International Journal of Information Retrieval Research (IJIRR) 4, no.3: 66-79. https://doi.org/10.4018/IJIRR.2014070105

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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.

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