A Lexicon Based Sentiment Analyzer Framework for Student-Teacher Textual Comments

Opinion mining is a process for tracking the mood of the people about any particular topic by review. Sentiment analysis tries to determine the sentiment of a writer about some aspect and also the overall contextual polarity of a document. This paper presents the sentiment analysis in collaboration with opinion extraction, summarization, and tracking the records of teachers. This paper modifies the existing algorithm in order to obtain the collaborated opinion result. The aim of this paper is to analyze the students’ text comments using lexicon based sentiment analysis to predict teacher performance. A database of sentiment words is created as a lexical source to get the polarity of words. In this study, students give their comments on their teacher. Finally, the result of opinion about the teachers are represented as very high, high, moderate, low and very low by evaluating the feelings expressed by students.