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Communities & Collections
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Does the Strength of Sentiment Matter? A Regression Based Approach on Turkish Social Media
Date
2017-06-23
Author
Ertugrul, Ali Mert
Onal, Itir
Acartürk, Cengiz
Metadata
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This work is licensed under a
Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
.
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Social media posts are usually informal and short in length. They may not always express their sentiment clearly. Therefore, multiple raters may assign different sentiments to a tweet. Instead of employing majority voting which ignores the strength of sentiments, the annotation can be enriched with a confidence score assigned for each sentiment. In this study, we analyze the effect of using regression on confidence scores in sentiment analysis using Turkish tweets. We extract hand-crafted features including lexical features, emoticons and sentiment scores. We also employ word embedding of tweets for regression and classification. Our findings reveal that employing regression on confidence scores slightly improves sentiment classification accuracy. Moreover, combining word embedding with hand-crafted features reduces the feature dimensionality and outperforms alternative feature combinations.
Subject Keywords
Sentiment analysis
,
Regression
,
Word embedding
,
Word2vec
URI
https://hdl.handle.net/11511/32610
DOI
https://doi.org/10.1007/978-3-319-59569-6_16
Collections
Graduate School of Informatics, Conference / Seminar