Sentiment retrieval on web reviews using spontaneous natural speech
J.C. Pereira, J. Luque, X. Anguera,¨Sentiment retrieval on web reviews using spontaneous natural speech¨ in Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing,(ICASSP’14), May 2014
This paper addresses the problem of document retrieval based on sentiment polarity criteria. A query based on natural spontaneous speech, expressing an opinion about a certain topic, is used to search a repository of documents containing favorable or unfavorable opinions. The goal is to retrieve documents whose opinions more closely resemble the one in the query. A semantic system based on the speech transcripts is augmented with information from full-length text articles. Posterior probabilities extracted from the article are used to regularize their transcription counterparts. This paper makes three important contributions. First, we introduce a framework for polarity analysis of sentiments that can accommodate combinations of different modalities, while maintaining the flexibility of unimodal systems, i.e. capable of dealing with the absence of any modality. Second, we show that it is possible to improve average precision on speech transcriptions’ sentiment retrieval by means of regularization. Third, we demonstrate the strength and generalization of our approach by training regularizers on one dataset, while performing sentiment retrieval experiments, with substantial gains, on a collection of YouTube clips.