Extracting Phrases Describing Problems with Products and Services from Twitter Messages

Abstract

Abstract. Social media contain many types of information useful to businesses. In this paper we discuss a trigger-target based approach to extract descriptions of problems from Twitter data. It is important to note that the descriptions of problems are factual statements as opposed to subjective opinions about products/services. We first identify the problem tweets i.e. the tweets containing descriptions of problems. We then extract the phrases that describe the problem. In our approach such descriptions are extracted as a combination of trigger and target phrases. Triggers are mostly domain independent verb phrases and are identified by using hand crafted lexical and syntactic patterns. Targets on the other hand are domain specific noun phrases syntactically related to the triggers. We frame the problem of finding target phrase corresponding to a trigger phrase as a ranking problem and show the results of experiments with maximum entropy classifiers and voted perceptrons. Both approaches outperform the rule based approach reported before.