Unsupervised method for sentiment analysis in online texts

• Method to predict sentiment in informal texts using unsupervised dependency parsing.
• Algorithm based on sentiment propagation using linguistic content without training.
• Method to create lexicon using polarity expansion algorithm for specific domains.
• Our method compares favorably well with other unsupervised and supervised methods.

In recent years, the explosive growth of online media, such as blogs and social networking sites, has enabled individuals and organizations to write about their personal experiences and express opinions. Classifying these documents using a polarity metric is an arduous task. We propose a novel approach to predicting sentiment in online textual messages such as tweets and reviews, based on an unsupervised dependency parsing-based text classification method that leverages a variety of natural language processing techniques and sentiment features primarily derived from sentiment lexicons. These lexicons were created by means of a semiautomatic polarity expansion algorithm in order to improve accuracy in specific application domains. The results obtained for the Cornell Movie Review, Obama-McCain Debate and SemEval-2015 datasets confirm the competitive performance and the robustness of the system.