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        <title>Sentiment Analysis Based on Probabilistic Models Using Inter-Sentence Information</title>
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            <title>Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC‘08)</title>
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            <namePart type="given">Nicoletta</namePart>
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    <abstract>This paper proposes a new method of the sentiment analysis utilizing inter-sentence structures especially for coping with reversal phenomenon of word polarity such as quotation of other’s opinions on an opposite side. We model these phenomenon using Hidden Conditional Random Fields(HCRFs) with three kinds of features: transition features, polarity features and reversal (of polarity) features. Polarity features and reversal features are doubly added to each word, and each weight of the features are trained by the common structure of positive and negative corpus in, for example, assuming that reversal phenomenon occured for the same reason (features) in both polarity corpus. Our method achieved better accuracy than the Naive Bayes method and as good as SVMs.</abstract>
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%0 Conference Proceedings
%T Sentiment Analysis Based on Probabilistic Models Using Inter-Sentence Information
%A Sadamitsu, Kugatsu
%A Sekine, Satoshi
%A Yamamoto, Mikio
%Y Calzolari, Nicoletta
%Y Choukri, Khalid
%Y Maegaard, Bente
%Y Mariani, Joseph
%Y Odijk, Jan
%Y Piperidis, Stelios
%Y Tapias, Daniel
%S Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC‘08)
%D 2008
%8 May
%I European Language Resources Association (ELRA)
%C Marrakech, Morocco
%F sadamitsu-etal-2008-sentiment
%X This paper proposes a new method of the sentiment analysis utilizing inter-sentence structures especially for coping with reversal phenomenon of word polarity such as quotation of other’s opinions on an opposite side. We model these phenomenon using Hidden Conditional Random Fields(HCRFs) with three kinds of features: transition features, polarity features and reversal (of polarity) features. Polarity features and reversal features are doubly added to each word, and each weight of the features are trained by the common structure of positive and negative corpus in, for example, assuming that reversal phenomenon occured for the same reason (features) in both polarity corpus. Our method achieved better accuracy than the Naive Bayes method and as good as SVMs.
%U https://aclanthology.org/L08-1619/

Markdown (Informal)

[Sentiment Analysis Based on Probabilistic Models Using Inter-Sentence Information](https://aclanthology.org/L08-1619/) (Sadamitsu et al., LREC 2008)

• Sentiment Analysis Based on Probabilistic Models Using Inter-Sentence Information (Sadamitsu et al., LREC 2008)

ACL

• Kugatsu Sadamitsu, Satoshi Sekine, and Mikio Yamamoto. 2008. Sentiment Analysis Based on Probabilistic Models Using Inter-Sentence Information. In Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC'08), Marrakech, Morocco. European Language Resources Association (ELRA).