Lexical Context for Profiling Reputation of Corporate Entities.

Opinion and trend mining on micro-blogs like Twitter recently attracted research interest in several fields including Information Retrieval (IR) and Natural Language Processing (NLP). However, the performance of existing approaches is limited by the quality of available training material. Moreover, explaining automatic systems' suggestions for decision support is a difficult task thanks to this lack of data. One of the promising solutions of this issue is the enrichment of textual content using large micro-blog archives or external document collections, e.g. Wikipedia. Despite some advantages in Reputation Dimension Classification (RDC) task pushed by RepLab, it remains a research challenge. In this paper we introduce a supervised classification method for RDC based on a threshold intersection graph. We analyzed the impact of various micro-blogs extension methods on RDC performance. We demonstrated that simple statistical NLP methods that do not require any external resources can be easily optimized to outperform the state-of-the-art approaches in RDC task. Then, the conducted experiments proved that the micro-blog enrichment by effective expansion techniques can improve classification quality. Lexical Context for Profiling Reputation of Corporate Entities. Available from: https://www.researchgate.net/publication/313846200_Lexical_Context_for_Profiling_Reputation_of_Corporate_Entities [accessed Jun 12, 2017].