Sensible Tweets: Speech Act Filtering for Effective Sentiment Analysis

Existing sentiment analyses of twitter data often develop ad hoc rules of selecting or discarding tweets. To move towards a rigorous methodology for the selection of short messages or so-called microtexts in sentiment analysis, we develop a sentiment analysis framework based on Speech Act theory wit...

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Bibliographic Details
Published in2022 6th International Conference on Business and Information Management (ICBIM) pp. 173 - 177
Main Authors Chee, Thad, Wu, Erika, Wu, Harris
Format Conference Proceeding
LanguageEnglish
Published IEEE 26.08.2022
Subjects
Blogs
Filtering
Information management
Media
Neural networks
Sentiment analysis
social media mining
Social networking (online)
speech act theory
tweet analysis
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Summary:Existing sentiment analyses of twitter data often develop ad hoc rules of selecting or discarding tweets. To move towards a rigorous methodology for the selection of short messages or so-called microtexts in sentiment analysis, we develop a sentiment analysis framework based on Speech Act theory with a focus on speech act filtering to identify sensible tweets - the sentiment-bearing microtexts that are perceptible to sentiment analysis. We develop a neural network classifier to implement speech act filtering and apply the sentimental analysis framework to twitter data. Using Twitter API, our speech act classifier and an online sentiment analysis tool, we conduct a pilot study on financial tweets to show that speech act filtering leads to higher quality sentiment analysis results.
DOI:10.1109/ICBIM57406.2022.00038