Entity/Event-Level Sentiment Detection and Inference

Abstract

Sentiment analysis aims at recognizing and understanding opinions expressed in languages. Previous work in sentiment analysis focused on extracting explicit opinions, which are directly expressed via sentiment words. However, opinions may be expressed implicitly via inferences over explicit sentiments. For example, in the sentence It is great that he was promoted. versus It is great that he was fired, there is an explicitly positive sentiment in both sentences because of the positive sentiment word great. Previous work may stop here. However, the sentiment toward he in the former sentence is positive, while the sentiment toward he in the later sentence is negative. The sentiments toward he in both sentences are implicit since there is no sentiment word directly modifying he. The implicit opinions are indicated in the text, and they are important for a sentiment analysis system to fully understand the documents. While previous work cannot recognize such implicit sentiment, this thesis contributes to developing an entity/event-level sentiment analysis system to recognize both explicit and implicit sentiments expressed from entities toward entities and events. Specifically, we first give the definitions of the entity/event-level sentiment analysis task. Since this is a new task, we develop two corpora serving as resources for this task. The implicit sentiments cannot be recognized merely relying on sentiment lexicons since the implicit sentiments are not directly associated with sentiment words. Inference rules are needed to recognize the implicit sentiments. Instead of developing a rule-based system to automatically infer implicit opinions, we develop computational models which use the inference rules as soft constraints. What’s more important, the models take into account the information not only from sentiment analysis tasks, but also from other Natural Language Processing tasks including information extraction and semantic role labeling. The models jointly solve different NLP tasks in one single model and improve the performances of the tasks. We also contribute to improving recognizing sources of opinions in this thesis. Finally, we conduct an analysis study showing that the idea of sentiment inference defined in this thesis can be applied to Chinese text as well.