Exploring Co-Training Strategies for Opinion Detection

For the last decade or so, sentiment analysis, which aims to automatically identify opinions, polarities, or emotions from user-generated content (e.g., blogs, tweets), has attracted interest from both academic and industrial communities. Most sentiment analysis strategies fall into 2 categories: lexicon-based and corpus-based approaches. While the latter often requires sentiment-labeled data to build a machine learning model, both approaches need sentiment-labeled data for evaluation. Unfortunately, most data domains lack sufficient quantities of labeled data, especially at the subdocument level. Semisupervised learning (SSL), a machine learning technique that requires only a few labeled examples and can automatically label unlabeled data, is a promising strategy to deal with the issue of insufficient labeled data. Although previous studies have shown promising results of applying various SSL algorithms to solve a sentiment-analysis problem, co-training, an SSL algorithm, has not attracted much attention for sentiment analysis largely due to its restricted assumptions. Therefore, this study focuses on revisiting co-training in depth and discusses several co-training strategies for sentiment analysis following a loose assumption. Results suggest that co-training can be more effective than can other currently adopted SSL methods for sentiment analysis.