Assigning a positive or negative score to a word out of context (i.e. a word’s prior polarity) is a challenging task for sentiment analysis. In the literature, various approaches based on SentiWordNet have been proposed. In this paper, we compare the most often used techniques together with newly proposed ones and incorporate all of them in a learning framework to see whether blending them can further improve the estimation of prior polarity scores. Using two different versions of SentiWordNet and testing regression and classification models across tasks and datasets, our learning approach consistently outperforms the single metrics, providing a new state-of-the-art approach in computing words’ prior polarity for sentiment analysis. We conclude our investigation showing interesting biases in calculated prior polarity scores when word Part of Speech and annotator gender are considered.

Sentiment analysis: How to derive prior polarities from SentiWordNet / Guerini, Marco; Gatti, Lorenzo; Turchi, M.. - (2013), pp. 1259-1269. ((Intervento presentato al convegno EMNLP 2013 tenutosi a Seattle nel 18th October-21st October 2013.

Sentiment analysis: How to derive prior polarities from SentiWordNet

Guerini, Marco;Gatti, Lorenzo;
2013

Abstract

Assigning a positive or negative score to a word out of context (i.e. a word’s prior polarity) is a challenging task for sentiment analysis. In the literature, various approaches based on SentiWordNet have been proposed. In this paper, we compare the most often used techniques together with newly proposed ones and incorporate all of them in a learning framework to see whether blending them can further improve the estimation of prior polarity scores. Using two different versions of SentiWordNet and testing regression and classification models across tasks and datasets, our learning approach consistently outperforms the single metrics, providing a new state-of-the-art approach in computing words’ prior polarity for sentiment analysis. We conclude our investigation showing interesting biases in calculated prior polarity scores when word Part of Speech and annotator gender are considered.
2013 Conference on Empirical Methods in Natural Language Processing: Proceedings of the Conference.
Stroudsburg, PA
The Association for Computational Linguistics
978-1-937284-97-8
Guerini, Marco; Gatti, Lorenzo; Turchi, M.
Sentiment analysis: How to derive prior polarities from SentiWordNet / Guerini, Marco; Gatti, Lorenzo; Turchi, M.. - (2013), pp. 1259-1269. ((Intervento presentato al convegno EMNLP 2013 tenutosi a Seattle nel 18th October-21st October 2013.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11572/169473
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