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Erschienen in: International Journal of Machine Learning and Cybernetics 8/2019

19.09.2017 | Original Article

Leveraging semantics for sentiment polarity detection in social media

verfasst von: Amna Dridi, Diego Reforgiato Recupero

Erschienen in: International Journal of Machine Learning and Cybernetics | Ausgabe 8/2019

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Abstract

With the increase use of microblogs and social media platforms as forms of on-line communication, we now have a huge amount of opinionated data reflecting people’s opinions and attitudes in form of reviews, forum discussions, blogs and tweets. This has recently brought great interest to sentiment analysis and opinion mining field that analyzes people’s feelings and attitudes from written language. Most of the existing approaches on sentiment analysis rely mainly on the presence of affect words that explicitly reflect sentiment. However, these approaches are semantically weak, that is, they do not take into account the semantics of words when detecting their sentiment in text. Only recently a few approaches (e.g. sentic computing) started investigating towards this direction. Following this trend, this paper investigates the role of semantics in sentiment analysis of social media. To this end, frame semantics and lexical resources such as BabelNet are employed to extract semantic features from social media that lead to more accurate sentiment analysis models. Experiments are conducted with different types of semantic information by assessing their impact in four social media datasets which incorporate tweets, blogs and movie reviews. A tenfold cross-validation shows that F1 measure increases significantly when using semantics in sentiment analysis in social media. Results show that the proposed approach considering word’s semantics for sentiment analysis surpasses non-semantic approaches for the considered datasets.

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Fußnoten
17
An abbreviation for retweet, which means citation or reposting of a message.
 
22
Post may refer to a movie review, a tweet, or a sentence from social blogs.
 
23
Please contact authors for open access to datasets.
 
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Metadaten
Titel
Leveraging semantics for sentiment polarity detection in social media
verfasst von
Amna Dridi
Diego Reforgiato Recupero
Publikationsdatum
19.09.2017
Verlag
Springer Berlin Heidelberg
Erschienen in
International Journal of Machine Learning and Cybernetics / Ausgabe 8/2019
Print ISSN: 1868-8071
Elektronische ISSN: 1868-808X
DOI
https://doi.org/10.1007/s13042-017-0727-z

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