The Distribution of Mood An Exploration of Distributional Compositions in Sentiment Classification

نویسنده

  • Jimmy Callin
چکیده

Distributional semantics is a research area investigating unsupervised datadriven models for quantifying semantic relatedness. This thesis investigates the possibilities of using distributional semantic models for sentiment classification of utterances, by composing distributional vectors of words in utterances. For evaluation I use a set of manually classified movie reviews. While the purpose of this study has been to test compositions in distributional semantic model, the work has mainly been focused on finding a useful model configuration for the DSM. The thesis concludes that more associative window sizes performed better than less associative ones. Weighting the DSM by PPMI gave the most stable performance improvements as well. Context selection is essential for achieving higher scores. While DSM does not reach beyond baseline results in its evaluation, there are still unexplored areas in which potential improvements may lie.

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تاریخ انتشار 2014