Sentiment analysis via dependency parsing

Nowadays, Sentiment Analysis (SA) is receiving huge attention because of the wide range of its direct applications like analyses of products, customer profiles, political trends, and so forth. Still, the availability of big amounts of data coming from the World Wide Web makes easier the study of both new techniques and evaluation methods. Current literature mainly focuses on two approaches which rely on sentiment lexicons (i.e., lists of words associated to scores of sentiment polarity) or on Natural Language Processing techniques (NLP). In this paper, on one hand, we introduce and evaluate a novel algorithm for SA that relies on a simple set of propagation rules applied at syntactic level within a dependency parse tree. On the other hand, we propose a context-based model where the users' sentiments (or opinions) are tuned according to some context of analysis. Finally, we present the system called SentiVis which implements these ideas through an orthogonal approach to SA that directly leans on Data Visualization. Extracted sentiments, with respect to some query of analysis, are ordered and represented graphically in a 2-dimensional space, conveying information about their strength and variability. This way, we avoid cumbersome rankings of objects and associated opinions by directly mapping such information on the screen. The user is then able to interact with the visualized data in order to discover interesting facts as well as removing false positive (or negative) opinions deriving by the used algorithm. We then evaluate the efficacy of the proposed system through several case studies.

► It introduces an approach for Sentiment Analysis based on Dependency Parsing
► It models an extraction method to retrieve opinions based on user queries
► It is based on a dataset of restaurant reviews that have been manually annotated
► It contains an evaluation that demonstrates its superiority with current approaches
► It proposes an interactive system called SentiVis for Sentiment Analysis.