Sentiment Analysis on Verbatim Responses for Understanding Students Living and Learning Experience

Abstract

Sentimental Analysis the computational study of sentiments, people’s opinions and emotions expressed in written language. It is very popular because of the wide range of applications it possesses. Opinions are the key influences of our behaviours and decision-making. Opinion mining helps in taking the right decisions as the analyzed data reports give the right emotion on products, companies, services, public personalities, governments, etc. Students’ opinions on their institutions, teachers, and trainers are collected nowadays. But the collected opinions are not useful unless it is analyzed and the measures according to it are taken. The conventional method is followed by analyzing it manually. Yet it could not be the consistent method for time and resource saving. Hence, we go for the computational method for opinion mining using Machine Learning techniques for extracting the emotions of the students. Machine Learning algorithms have come a long way, with Naive Bayes, Support Vector Machine and Maximum Entropy is the feature used in research. Sentiment classification by different categories involving the sentiments is the topic of research. This paper presents the survey on Sentimental Analysis on students’ feedback to extract the emotions of the students with the text feedback.

Keywords: Sentimental Analysis (SA), Opinion Mining, Machine Learning (ML), Natural Language Processing (NLP), Student Feedback Analysis

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