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ABSTRACT:
In education system, student’s feedback is important to measure the quality of teaching.Students’ feedback can be analyzed using lexicon-based approach to identify the student’s positive or negative attitude. The main objective of this research is to analysis the Students feedback and obtains the opinion. In most of the existing teaching evaluation system, the intensifier words and blind negation words are not considered. The level of opinion result isn’t displayed-whether positive or negative opinion. To address this problem, we propose to analyze the student’s text feedback automatically using lexicon based approach to predict the level of teaching performance. The opinion result is represented as whether strongly positive, moderately positive, weakly positive, strongly negative, moderately negative, weakly negative or neutral.

Key words: Opinion mining, Sentiment Analysis, Teaching Evaluation, Lexicon based
approach, corpus based approach, Dictionary based approach, Qualitative information,
quantitative information, Afinn lexicon

INTRODUCTION:
Sentimental analysis is a method for identifying the sentiment expressed in texts. The need of Sentiment Analysis of text has gained more importance in today’s situations faced by the people of the world. Generally, there are three approaches in sentimental analysis. They are lexicon based, machine learning and hybrid approach. In machine learning technique, it uses unsupervised learning or supervised learning. Classification problem can be carried out using several algorithms like support vector machine, naive bayes, random forest. In lexicon based method sentiment polarity of the textual content is detected using sentiment lexicon. A lexicon is a list of words with associated sentiment polarity Sentiment analysis is a process for tracking the mood of the people about any particular topic by review. In general, opinion may be the result of people’s personal feelings, beliefs, sentiments and desires etc. This research work focus on students comments Analyzing students comments using sentiment analysis approaches can classify the students positive or negative feelings. Student’s feedback can highlight various issues students may have with a lecture. Sometimes students do not understand what the lecturer is trying to explain, thus by providing feedbacks, students can indicate this to the lecturer. The Input we take is qualitative data rather than quantitative data. The processing of qualitative data analysis is very important and it can enhance the teacher evaluation effectiveness. Evaluating performance of faculty members is becoming an essential
component of an education management system. It not only helps in improving the course contents and quality but is also often used during the annual appraisal process of faculty members. The evaluation is typically collected at the end of each course on a set of question which are answered. The evaluation form, however, also provides room for open feedback which typically is not included in the performance evaluation/appraisal due to lack of automated text analytics methods. The textual data may contain useful insight about subject knowledge of the teacher, regularity, and presentation skills and may also provide suggestions to improve the teaching of quality. Students provide feedback in quantitative ratings and
qualitative comments related to preparation, contents, delivery methods, punctual, skills,appreciation, and learning experience. The delivery methods and preparation component refers to instructor’s interaction, delivery style, ability to motivate students, out of class support, etc. The content refers to course details such as concepts, lecture notes, labs, exams, projects, etc.The preparation refers to student’s learning experience such as understanding concepts, developing skills, applying acquired skills, etc. The paper correction refers to correction of mistakes and providing solutions to overcome it. The punctual refers to the class timing and assignment or record submission. The appreciation refers to the comments given when something is done perfectly. Analyzing and evaluating this qualitative data helps us to make better sense of student feedback on instruction and curriculum. Recent methods for analyzing student course evaluations are manual and it mainly focuses on the quantitative feedback. It does not support for deeper analysis. This paper focus on providing qualitative and quantitative feedback to analyze and provide better teaching to improve the student’s performance.

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