The conversational element of Twitter has recently become of particular interest to the marketing community. However, most studies on mining product features through Twitter, have so far employed simple individual tweets rather than considering the whole conversations. In this paper, we empirically evaluate whether employing user interactions in public conversations can improve the product feature extraction from tweets. We propose a conversation-based method which considers a conversation as a reply tree and employs reply links, to effectively extract the product features involved in the messages. We also develop a conversation filtering process which combines scores measured from different aspects including content relevance and social aspects. 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.
