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Proceedings of the Seventh International AAAI Conference on Weblogs and Social Media Sentiment Prediction Using Collaborative Filtering
BibTeX
@MISC{Kim_proceedingsof,
author = {Jihie Kim and Jaebong Yoo and Ho Lim and Huida Qiu and Zornitsa Kozareva and Aram Galstyan},
title = {Proceedings of the Seventh International AAAI Conference on Weblogs and Social Media Sentiment Prediction Using Collaborative Filtering},
year = {}
}
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Abstract
Learning sentiment models from short texts such as tweets is a notoriously challenging problem due to very strong noise and data sparsity. This paper presents a novel, collaborative filtering-based approach for sentiment prediction in twitter conversation threads. Given a set of sentiment holders and sentiment targets, we assume we know the true sentiments for a small fraction of holder-target pairs. This information is then used to predict the sentiment of a previously unknown user towards another user or an entity using collaborative filtering algorithms. We validate our model on two Twitter datasets using different collaborative filtering techniques. Our preliminary results demonstrate that the proposed approach can be effectively used in twitter sentiment prediction, thus mitigating the data sparsity problem.
Keyphrases
seventh international aaai conference sentiment model preliminary result strong noise twitter conversation thread true sentiment holder-target pair collaborative filtering-based approach data sparsity short text small fraction twitter datasets sentiment holder twitter sentiment prediction sentiment prediction unknown user towards collaborative filtering algorithm data sparsity problem sentiment target different collaborative filtering technique
