Sentiment classification gains a lot of attention nowadays. For a university, the knowledge obtained from classifying sentiments of student learning in courses is highly valuable, and can be used to help teachers improve their teaching skills. In this research, sentiment classification based on text embedding is applied to enhance the performance of sentiment classification for Thai teaching evaluation. Text embedding techniques considers both syntactic and semantic elements of sentences that can be used to improve the performance of the classification. This research uses two approaches to apply text embedding for classification. The first approach uses fastText classification. According to the results, fastText provides the best overall performance; its highest F-measure was at 0.8212. The second approach constructs text vectors for classification using traditional classifiers. This approach provides better performance over TF-IDF for k-nearest neighbors and naïve Bayes. For naïve Bayes, the second approach yields the best performance of geometric mean at 0.8961. The performance of TF-IDF is better suited to using decision tree than the second approach. The benefit of this research is that it presents the workflow of using text embedding for Thai teaching evaluation to improve the performance of sentiment classification. By using embedding techniques, similarity and analogy tasks of texts are established along with the classification.
Less Sparse Feature Set with Meta Heuristic Weighted Classifier for Tweet Sentiment Classification
