Inferring spread of readers’ emotion affected by online news
Depending on the reader, A news article may be viewed from many different perspectives, thus triggering different (and possibly contradicting) emotions. In this paper, we formulate a problem of predicting readers’ emotion distribution affected by a news article. Our approach analyzes affective annot...
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sg-smu-ink.sis_research-49632020-03-26T07:19:40Z Inferring spread of readers’ emotion affected by online news SULISTYA, Agus THUNG, Ferdian LO, David Depending on the reader, A news article may be viewed from many different perspectives, thus triggering different (and possibly contradicting) emotions. In this paper, we formulate a problem of predicting readers’ emotion distribution affected by a news article. Our approach analyzes affective annotations provided by readers of news articles taken from a non-English online news site. We create a new corpus from the annotated articles, and build a domain-specific emotion lexicon and word embedding features. We finally construct a multi-target regression model from a set of features extracted from online news articles. Our experiments show that by combining lexicon and word embedding features, our regression model is able to predict the emotion distribution with RMSE scores between 0.067 to 0.232 for each emotion category. 2017-09-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/3961 info:doi/10.1007/978-3-319-67217-5_26 https://ink.library.smu.edu.sg/context/sis_research/article/4963/viewcontent/ReaderEmotion_2017_afv.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Social emotion Multi target regression Machine learning Social Media Software Engineering |
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Social emotion Multi target regression Machine learning Social Media Software Engineering SULISTYA, Agus THUNG, Ferdian LO, David Inferring spread of readers’ emotion affected by online news |
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Depending on the reader, A news article may be viewed from many different perspectives, thus triggering different (and possibly contradicting) emotions. In this paper, we formulate a problem of predicting readers’ emotion distribution affected by a news article. Our approach analyzes affective annotations provided by readers of news articles taken from a non-English online news site. We create a new corpus from the annotated articles, and build a domain-specific emotion lexicon and word embedding features. We finally construct a multi-target regression model from a set of features extracted from online news articles. Our experiments show that by combining lexicon and word embedding features, our regression model is able to predict the emotion distribution with RMSE scores between 0.067 to 0.232 for each emotion category. |
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SULISTYA, Agus THUNG, Ferdian LO, David |
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SULISTYA, Agus THUNG, Ferdian LO, David |
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SULISTYA, Agus |
title |
Inferring spread of readers’ emotion affected by online news |
title_short |
Inferring spread of readers’ emotion affected by online news |
title_full |
Inferring spread of readers’ emotion affected by online news |
title_fullStr |
Inferring spread of readers’ emotion affected by online news |
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Inferring spread of readers’ emotion affected by online news |
title_sort |
inferring spread of readers’ emotion affected by online news |
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Institutional Knowledge at Singapore Management University |
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2017 |
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https://ink.library.smu.edu.sg/sis_research/3961 https://ink.library.smu.edu.sg/context/sis_research/article/4963/viewcontent/ReaderEmotion_2017_afv.pdf |
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