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Fine-grained sentiment analysis in text and multi-party conversation

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The past several years have seen a huge growth in research on identifying and characterizing opinions and sentiments in text. While much of this work has focused on classifying the sentiment of documents, a more fine-grained analysis at the sentence level and below is needed for any application that seeks detailed opinion information, e.g., opinion question answering. In this talk, I will present work on fine-grained sentiment analysis in both text and multi-party conversation. The approaches taken are quite different in terms of the features explored. For text, a wide range of linguistically motivated features are employed for determining when instances of polarity terms are indeed being used to express positive and negative sentiments in context. The results of this disambiguation are then used in determining sentence polarity. For sentiment analysis in speech, the focus is on exploiting very shallow linguistic features, such as n-grams of characters and phonemes, for classifying the subjectivity and sentiment of utterances.

This talk is part of the NLIP Seminar Series series.

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