Sentiment Analysis of Online Spoken Reviews

This paper describes several experiments in building a sentiment analysis classifier for spoken reviews. We specifically focus on the linguistic component of these reviews, with the goal of understanding the difference in sentiment classification performance when using manual versus automatic transcriptions, as well as the difference between spoken and written reviews. We introduce a novel dataset, consisting of video reviews for two different domains (cellular phones and fiction books), and we show that using only the linguistic component of these reviews we can obtain sentiment classifiers with accuracies in the range of 65-75%.