Application of Sentiment Analysis to Language Learning

Abstract

Emotion vocabulary has been studied in various disciplines, such as psychology, linguistics, and computational linguistics. Recently, it plays a requisite role in sentiment analysis or opinion mining. However, emotion vocabulary has not received considerable attention in second or foreign language learning. The insufficient pedagogical materials and inefficient tool support seem to provide little help for learners to master emotion words. The current study considers the application of sentiment analysis to language learning. To achieve this goal, we developed RESOLVE, a context-aware emotion synonym suggestion system, for educational purposes. Utilizing machine-learning techniques, the system is capable of suggesting synonymous emotion words appropriate to learners' contexts. Importantly, the usage information of each emotion word, including scenario descriptions, definitions, and example sentences, is provided in order to help develop language learners' vocabulary knowledge as well as help facilitate their word use. A pedagogical evaluation of the system's effectiveness was conducted using a writing task and a survey questionnaire. The results indicate that the participants achieved substantial progress on emotion word use with the help of the proposed system. In particular, less proficient participants demonstrated greater improvements. Meanwhile, participants showed positive attitudes toward the tool support, as it helps them to have a better command of emotion words in their writings.

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