NEURAL FINE-GRAINED SENTIMENT ANALYSIS WITH UNSUPERVISED AND TRANSFER LEARNING APPROACHES

Abstract

We study the problem of aspect-based sentiment analysis, also referred to as fine- grained sentiment analysis, which is an important area in sentiment analysis that has seen a lot of research effort and real-world applications. Different from document-level and sentence-level sentiment analysis which only assigns an overall polarity score to a piece of input text, aspect-level analysis is based on the idea that an opinion should include a sentiment and a target; therefore, it aims to identify the sentiment-target pairs from a given text. For example, the review sentence “Great food but the service is dreadful” evaluates two targets – “food” (positive) and “service” (negative). The development of aspect-based sentiment analysis systems generally faces two major challenges. First, this problem is naturally more difficult compared to coarse- grained sentiment analysis because more fine-grained features are needed for aspect- level predictions. Second, the training resources for this task are limited as it is expensive to obtain fine-grained annotated data. Therefore, in this thesis, we focus on two objectives: (1) designing flexible and effective models for fine-grained sentiment analysis; (2) leverage cheaply available resources, such as unlabeled data and transfer learning approaches.