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Semantic patterns for sentiment analysis of Twitter

Saif, Hassan; He, Yulan; Fernández, Miriam and Alani, Harith (2014). Semantic patterns for sentiment analysis of Twitter. In: The Semantic Web – ISWC 2014, Springer International Publishing, pp. 324–340.

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DOI (Digital Object Identifier) Link: https://doi.org/10.1007/978-3-319-11915-1_21
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Abstract

Most existing approaches to Twitter sentiment analysis assume that sentiment is explicitly expressed through affective words. Nevertheless, sentiment is often implicitly expressed via latent semantic relations, patterns and dependencies among words in tweets. In this paper, we propose a novel approach that automatically captures patterns of words of similar contextual semantics and sentiment in tweets. Unlike previous work on sentiment pattern extraction, our proposed approach does not rely on external and fixed sets of syntactical templates/patterns, nor requires deep analyses of the syntactic structure of sentences in tweets. We evaluate our approach with tweet- and entity-level sentiment analysis tasks by using the extracted semantic patterns as classification features in both tasks. We use 9 Twitter datasets in our evaluation and compare the performance of our patterns against 6 state-of-the-art baselines. Results show that our patterns consistently outperform all other baselines on all datasets by 2.19% at the tweet-level and 7.5% at the entity-level in average F-measure.

Item Type: Conference or Workshop Item
Copyright Holders: 2014 Springer International Publishing Switzerland
ISBN: 3-319-11914-1, 978-3-319-11914-4
ISSN: 0302-9743
Keywords: sentiment analysis; semantic patterns; Twitter
Academic Unit/School: Faculty of Science, Technology, Engineering and Mathematics (STEM) > Knowledge Media Institute (KMi)
Faculty of Science, Technology, Engineering and Mathematics (STEM)
Research Group: Centre for Research in Computing (CRC)
Related URLs:
Item ID: 41399
Depositing User: Hassan Saif
Date Deposited: 26 Nov 2014 10:33
Last Modified: 20 Dec 2017 16:53
URI: http://oro.open.ac.uk/id/eprint/41399
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