Context-specific sentiment lexicon expansion via minimal user interaction

One of the important factors in the performance of sentiment analysis methods is having a comprehensive sentiment lexicon. However, since sentiment words have different polarities not only in different domains, but also in different contexts within the same domain, constructing such context-specific sentiment lexicons is not an easy task. The high costs of manually constructing such lexicons motivate researchers to create automatic methods for finding sentiment words and assigning their polarities. However, existing methods may encounter ambiguous cases with contradictory evidence which are hard to automatically resolve. To address this problem, we aim to engage the user in the process of polarity assignment and improve the quality of the generated lexicon via minimal user effort. A novel visualization is employed to present the results of the automatic algorithm, i.e., the extracted sentiment pairs along with their polarities. User interactions are provided to facilitate the supervision process. The results of our user study demonstrate (1) involving the user in the polarity assignment process improves the quality of the generated lexicon significantly, and (2) participants in the study preferred our visual interface and conveyed that it is easier to use compared to a text-based interface.