On Enhancing the Label Propagation Algorithm for Sentiment Analysis Using Active Learning with an Artificial Oracle

A core component of Sentiment Analysis is the generation of sentiment lists. Label propagation is equivocally one of the most used approaches for generating sentiment lists based on annotated seed words in a manual manner. Words which are situated many hops away from the seed words tend to get low sentiment values. Such inherent property of the Label Propagation algorithm poses a controversial challenge in sentiment analysis. In this paper, we propose an iterative approach based on the theory of Active Learning [1] that attempts to remedy to this problem without any need for additional manual labeling. Our algorithm is bootstrapped with a limited amount of seeds. Then, at each iteration, a fixed number of "informative words" are selected as new seeds for labeling according to different criteria that we will elucidate in the paper. Subsequently, the Label Propagation is retrained in the next iteration with the additional labeled seeds. A major contribution of this article is that, unlike the theory of Active Learning that prompts the user for additional labeling, we generate the additional seeds with an Artificial Oracle. This is radically different from the main stream of Active Learning Theory that resorts to a human (user) as oracle for labeling those additional seeds. Consequently, we relieve the user from the cumbersome task of manual annotation while still achieving a high performance. The lexicons were evaluated by classifying product and movie reviews. Most of the generated sentiment lexicons using Active learning perform better than the Label Propagation algorithm.