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Term weighting metrics assign weights to terms in order to discriminate theimportant terms from the less crucial ones. Due to this characteristic, thesemetrics have attracted growing attention in text classification and recently insentiment analysis. Using the weights given by such metrics could lead to moreaccurate document representation which may improve the performance of theclassification. While previous studies have focused on proposing or comparingdifferent weighting metrics at two-classes document level sentiment analysis,this study propose to analyse the results given by each metric in order to findout the characteristics of good and bad weighting metrics. Therefore we presentan empirical study of fifteen global supervised weighting metrics with fourlocal weighting metrics adopted from information retrieval, we also give ananalysis to understand the behavior of each metric by observing and analysinghow each metric distributes the terms and deduce some characteristics which maydistinguish the good and bad metrics. The evaluation has been done usingSupport Vector Machine on three different datasets: Twitter, restaurant andlaptop reviews.

Supervised Term Weighting Metrics for Sentiment Analysis in Short Text