Identification of fine grained feature based event and sentiment phrases from business news stories

The analysis of business/financial news has become a popular area of research because of the possibility to infer the future prospects of companies, economies and economic actors in general on information contained in the media. The classical approaches rely upon a "coarse" polarity classification of a news story, however this may not be an optimal solution because this form of classification assigns the same polarity to all of the entities contained in the news story. A news story which contains multiple entities may contain varying polarity for each individual entity. In addition,"coarse" classification may ignore sentiment modifiers which may alter the strength or direction of the story's polarity. News stories don't have a preassigned polarity label, consequently news stories must be manually assigned a polarity label. This process is slow, therefore there will be limited labelled data available. This lack of pre-classified data may inhibit the performance of learners which rely upon labelled data.

This paper describes a rule based approach which identifies feature based sentiment and business event phrases. The phrases are captured with context free grammars which model the phrase as a triple. The triple contains: 1. Phrase subject (an economic actor), 2. A sentiment adjective or event verb and 3. An object (a property of the phrase subject). The captured phrases are limited by the semantic role of the subject. An annotated phrase can capture sentiment modifiers and negators. The scoring of the phrase incorporates all relevant linguistic features and consequently an accurate individual polarity score can be assigned to each relevant entity. The evaluation of the technique reports a recall of 0.71 and precision of 0.94 sentiment phrase annotation and 0.84 recall and 0.83 precision for event phrase annotation.