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Experimental Evaluation of Algorithms for Connected Speech Recognition Using Hidden Markov Models.
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In: DTIC AND NTIS (1987)
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| 158 |
A comparison of domain-based word polarity estimation using different word embeddings
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Abstract:
Comunicació presentada a la Tenth International Conference on Language Resources and Evaluation (LREC 2016), celebrada els dies 23 a 28 de maig de 2016 a Portorož, Eslovènia. ; A key point in Sentiment Analysis is to determine the polarity of the sentiment implied by a certain word or expression. In basic Sentiment Analysis systems this sentiment polarity of the words is accounted and weighted in different ways to provide a degree of positivity/negativity. Currently words are also modelled as continuous dense vectors, known as word embeddings, which seem to encode interesting semantic knowledge. With regard to Sentiment Analysis, word embeddings are used as features to more complex supervised classification systems to obtain sentiment classifiers. In this paper we compare a set of existing sentiment lexicons and sentiment lexicon generation techniques. We also show a simple but effective technique to calculate a word polarity value for each word in a domain using existing continuous word embeddings generation methods. Further, we also show that word embeddings calculated on in-domain corpus capture the polarity better than the ones calculated on general-domain corpus. ; This work has been supported by Vicomtech-IK4 and partially funded by TUNER project (TIN2015-65308-C5-1-R).
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Keyword:
Sentiment analysis; Sentiment lexicon; Word embedding
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URL: http://hdl.handle.net/10230/33709
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