Sentiment Analysis Using Collaborated Opinion Mining
This work addresses sentiment analysis for student evaluation in education, but it is incremental as it modifies an existing algorithm without introducing a new paradigm.
The paper tackles the problem of analyzing student performance by proposing a sentiment analysis algorithm that collaborates with opinion extraction, summarization, and tracking, applied to teacher remarks to categorize opinions into levels such as very high, high, moderate, low, and very low.
Opinion mining and Sentiment analysis have emerged as a field of study since the widespread of World Wide Web and internet. Opinion refers to extraction of those lines or phrase in the raw and huge data which express an opinion. Sentiment analysis on the other hand identifies the polarity of the opinion being extracted. In this paper we propose the sentiment analysis in collaboration with opinion extraction, summarization, and tracking the records of the students. The paper modifies the existing algorithm in order to obtain the collaborated opinion about the students. The resultant opinion is represented as very high, high, moderate, low and very low. The paper is based on a case study where teachers give their remarks about the students and by applying the proposed sentiment analysis algorithm the opinion is extracted and represented.