Towards Using Public Conversations to Mine Product Features in Twitter

While public conversations in Twitter have gained increasing interest in the marketing sector, relatively very little data-mining research have been conducted in this area. In this paper, we empirically evaluate whether employing reply links in public conversations can enhance the product feature extraction from tweets. We introduce a conversation-based method that considers a conversation as a reply tree and employs anaphora resolution in a backtracking mechanism to effectively extract the product features involved in the messages. We also develop a conversation filtering process based on a set of filtering measures including content relevance and social metrics. We conducted our experiments using a manually annotated Twitter corpus involving smartphones and other electronics products. The experimental results show the effectiveness of our proposed method.