Sentiment Discovery of Social Messages Using Self-Organizing Maps

Introduction Predicting the sentiments and emotions of people from their texts is a critical issue in cognitive computing. The explosive growth of social network services has led to a tremendous increase of textual data, increasing the demand of the advanced analysis of these data. Sentiment analysis on textual social media data emerged in recent years to fulfill the needs of areas such as national security, business, politics, and economics; however, text messages from social networks are rather different from those of traditional text documents, especially in presentation style and lengths. Therefore, it is difficult but essential to develop an effective method to explore the sentiments of social messages. Methods In this study, we first applied a self-organizing map (SOM) algorithm to cluster social messages as well as sentiment keywords. An association discovery process was then applied to discover the associations between a message and some sentiment keywords, and the sentiment of a message was determined according to such associations. Results We performed experiments on collected Twitter messages and the results’ accuracy outperformed that of a similar approach. Conclusions A sentiment analysis approach based on SOMs was proposed. The associations between messages and keywords were derived using the proposed method. The novelty of this work arises from the adoption of association discovery process in sentiment analysis.