Sentiment analysis of text based on emoji attention mechanisms: a new approach to online course evaluation

Course evaluation has evolved into a significant metric of educational quality given the explosive growth of online learning. Traditional sentiment analysis techniques still have some difficulties handling multidimensional sentiment expressions in online course evaluations even if they have some success with social media and review data. In order to increase the accuracy of sentiment analysis in online course evaluation texts, we present in this work a textual sentiment analysis approach based on the attention mechanism of emoticons. First, this work integrates text information with emoji as a necessary input feature for sentiment analysis to provide multimodal sentiment classification. Especially when dealing with online course evaluation texts with several sentiment dimensions, which has great benefits and provides more accurate course evaluation data for online education platforms, the model suggested performs well on several benchmark datasets and outperforms conventional sentiment analysis methods by means of experimental validation.