A Framework for Constructing Thai Sentiment Corpus using the Cosine Similarity Technique

Unstructured data is growing rapidly due to the increase in various social media that allow individuals to express and write reviews in either a formal or informal style. Thus, in sentiment analysis, it becomes difficult to identify and analyze both positive and negative reviews. Thai is a low-resource language with few resources with which to conduct NLP research and the lack of a sentiment corpus. The main objective of this paper is to present a framework in which to construct a Thai sentiment corpus and sentiment polarity classification utilizing the cosine similarity technique. The proposed framework consists of three main steps: data collection; data preprocessing; and sentiment similarity measurement. Initially, data collection generates a sub-step that is a manual sentiment that classifies polarity into positive and negative. Data preprocessing is then applied. Moreover, we also created a special database in which to store text tokenization, convert abbreviations, check spelling errors, and conduct stop-word removal. Lastly, sentiment similarity measurement was conducted between two reviews with a combination of TF-IDF and the cosine similarity technique. We evaluated our framework by employing training data incorporating 3,129 reviews and testing data comprised of 1,000 reviews. The experiment results demonstrated that the proposed framework achieved an accuracy of 81.2%. We further observed that the data preprocessing step herein significantly affected the accuracy of the sentence similarity measurements of the two reviews.