Sentiment Classification for the Italian Language: a Case Study on Movie Reviews
We consider the problem of tracking the opinion polarity, in terms of positive or negative orientation, expressed in documents written in natural language and extracted from a heterogeneous set of Web sources. More specifically, we focus our attention on the movie reviews domain. We are interested in evaluating the performance obtained by a set of high performance opinion polarity classifiers for the Italian language. Classification of polarity expressed by the input documents is achieved by means of several sets of specialized autonomous or interacting agents, devoted, respectively, to document gathering, classification and visualization. In particular the results of opinion analysis are represented by means of a graphical interface, where a multi agent based implementation of zz-structures is exploited to offer graph-centric views and navigation of results. The specific experimental evaluation performed so far shows an accuracy level, which is higher than previous results reported in the literature.
opinion analysis zz-structures sentiment analysis cognitive agents machine learning