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Sentiment Classification of Social Media Content with Features Generated Using Topic Models

Blair, Stuart, Bi, Yaxin and Mulvenna, Maurice (2016) Sentiment Classification of Social Media Content with Features Generated Using Topic Models. In: Proceedings of the Eighth European Starting AI Researcher Symposium (STAIRS-2016). IOS Press, Amsterdam, pp. 155-165. ISBN 978-1-61499-681-1 [Book section]

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URL: http://ebooks.iospress.nl/ISBN/978-1-61499-682-8

DOI: 10.3233/978-1-61499-682-8-155

Abstract

This paper presents a method for using topic distributions generated from topic models as features for performing sentiment analysis on documents. This will be tested in the social media domain, specifically Twitter. The proposed approach allows for the mapping from word space to topic space which allows for less fea- tures to be needed and also reduces computational complexity. Multiple machine learning algorithms will be used to test the topic model generated features and a number of different versions of test corpus will be used, including unigrams, bi- grams, part-of-speech tagging and adjectives only. The method proposed will also be compared to other notable topic-sentiment methods such as the aspect-sentiment unification model and the joint sentiment/topic model. The results show that using topic distributions can improve the accuracy of classification algorithms, however, the performance can be dependent on the algorithm used and the initial features used. Additionally, we show that using only topics as features outperforms the hy- brid topic-sentiment models.

Item Type:Book section
Keywords:sentiment classification, topic models, social media, feature generation
Faculties and Schools:Faculty of Computing & Engineering
Faculty of Computing & Engineering > School of Computing and Mathematics
Research Institutes and Groups:Computer Science Research Institute
Computer Science Research Institute > Artificial Intelligence and Applications
ID Code:36022
Deposited By: Professor Maurice Mulvenna
Deposited On:04 Oct 2016 11:29
Last Modified:04 Oct 2016 11:29

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