Hypothesis testing based on observation from Thai sentiment classification - Netisopakul, Ponrudee; Pasupa, Kitsuchart; Lertsuksakda, Rathawut (My University, 2017)
This work focuses on error analyzes from the Support Vector Machine (SVM) classification on Thai children stories at a sentence level. The construction of the Sentiment Term Tagging System (STTS) program allows the researchers to make observations and hypothesize around the areas where most anomalies occur. Three hypotheses, based on terms sentiment chosen for SVM predictions, are evidently proved to hold. In addition, a number of ways to improve the Thai sentiment classification research are suggested, including considerations to add negation into the process, add weighing scheme for different part-of-speech, disambiguate word senses, and update the Thai sentiment resource.
Keywords: Error analysis; Hypothesis testing; SenticNet2; Sentiment analysis; Support vector machine