Sentiment analysis of student feedback using machine learning and lexicon based approaches

Faculty / School

Faculty of Computer Sciences (FCS)

Department

Department of Computer Science

Was this content written or created while at IBA?

Yes

Document Type

Conference Paper

Publication Date

8-3-2017

Conference Name

2017 International Conference on Research and Innovation in Information Systems (ICRIIS)

Conference Location

Langkawi, Malaysia

Conference Dates

16-17 July 2017

ISBN/ISSN

85029913876 (Scopus)

Issue

2324-8157

First Page

1

Last Page

6

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Abstract / Description

This paper presents a combination of machine learning and lexicon-based approaches for sentiment analysis of students feedback. The textual feedback, typically collected towards the end of a semester, provides useful insights into the overall teaching quality and suggests valuable ways for improving teaching methodology. The paper describes a sentiment analysis model trained using TF-IDF and lexicon-based features to analyze the sentiments expressed by students in their textual feedback. A comparative analysis is also conducted between the proposed model and other methods of sentiment analysis. The experimental results suggest that the proposed model performs better than other methods.

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