Experiments of Supervised Learning and Semi-Supervised Learning in Thai Financial News Sentiment: A Comparative Study

Abstract

Sentiment classification is an instrument of natural language processing tasks in text analysis to measure customer feedback from given documents such as product reviews, news, and texts. This research aims to experiment with Thai financial news sentiment classification and evaluate sentiment classification performance. In this research, we show financial news sentiment classification experimental results when comparing supervised and semi-supervised methods. In the research methodology, we use PyThaiNLP to tokenize and remove stopwords and split datasets into 85% of the training set and 15% of the testing set. Next, we classify sentiment using machine learning and deep learning approaches with feature extraction such as bag-of-words, term frequency–inverse document frequency, and word embedding (Word2Vec and Bidirectional Encoder Representations from Transformers (BERT)) in given texts. The results show that support vector machine with the BERT model yields the best performance at 83.38%; in contrast, the random forest classifier with bag-of-words yields the worst performance at 54.10% in the machine learning approach. Another experiment reveals that long short-term memory with the BERT model yields the best performance at 84.07% in contrast to the convolutional neural network with bag-of-words, which yields the worst performance at 69.80% in the deep learning approach. The results imply that support vector machine, convolutional neural network, and long short-term memory are suitable for classifying sentiment in complex structure language. From this study, we observe the importance of sentiment classification tools between supervised and semi-supervised learning, and we look forward to furthering this work.