A Method for Extracting Lexicon for Sentiment Analysis Based on Morphological Sentence Patterns

In these days, people share their emotions, opinions, and experiences of products or services using online review services on their comments, and the people concern the reviews to make decision when buying products or services. Sentiment analysis is one of the solution to observe and summarize emotional opinions from the data. In spite of high demands for developing sentiment analysis, the development of the sentiment analysis faces some challenges to analyze the data, because the data is unstructured, unlabeled, and noisy. The aspect-based sentiment analysis approach helps for more in-depth analysis, however building aspect and emotional expression is one of the challenge for the aspect-based sentiment analysis approach. Accordingly, we propose an unsupervised system for building aspect-expressions to minimize human-coding efforts. The proposed method uses morphological sentence patterns through an aspect-expression pattern recognizer. It guarantees relatively higher accuracy. As well as, we found some characteristics for selecting patterns to extracting aspect-expressions accurately. The greatest advantage of our system is performing without any human coded train-set.