Welcome To UTPedia

We would like to introduce you, the new knowledge repository product called UTPedia. The UTP Electronic and Digital Intellectual Asset. It stores digitized version of thesis, final year project reports and past year examination questions.

Browse content of UTPedia using Year, Subject, Department and Author and Search for required document using Searching facilities included in UTPedia. UTPedia with full text are accessible for all registered users, whereas only the physical information and metadata can be retrieved by public users. UTPedia collaborating and connecting peoples with university’s intellectual works from anywhere.

Disclaimer - Universiti Teknologi PETRONAS shall not be liable for any loss or damage caused by the usage of any information obtained from this web site.Best viewed using Mozilla Firefox 3 or IE 7 with resolution 1024 x 768.

SENTIMENT ANALYSIS TO CLASSIFY THE SENTIMENT OF STUDENTS’ FEEDBACK USING THE CLASSIFICATION METHOD IN R AND POWER BI

CLEMENT, RACHEL SALLY (2020) SENTIMENT ANALYSIS TO CLASSIFY THE SENTIMENT OF STUDENTS’ FEEDBACK USING THE CLASSIFICATION METHOD IN R AND POWER BI. IRC, Universiti Teknologi PETRONAS. (Submitted)

[img] PDF
Restricted to Registered users only

Download (1878Kb)

Abstract

Students’ feedback plays a huge important role in academic institutions to improve teaching practices and learning processes. Quantitative feedback is given a lot of attention in most universities as it is easier to be analysed. However, the qualitative comments given by the students are often put aside. The reason is that due to its unstructured messages form, it is difficult to analyse the feedback manually. To counter this problem, text mining technique which utilize machine learning approach will be implemented on students’ feedback to display the teaching assessment results. Machine learning approach such as Naïve Bayes classifier, has been commonly used for sentiment analysis to precisely and accurately describe the sentiment of an individual review. By using the Naïve Bayes classifier, the outcomes of the teaching assessment can be measured through defining the level of positive, neutral and negative opinions. The implementing process of text mining technique from students’ feedback will be explained accordingly by using the statistical programming tool, R. The teaching assessment results will be visualised and displayed in a dashboard through the utilization of Power BI. This study is conducted with the aim to better understand the sentiment analysis using Naïve Bayes classifier. The main objective of this study is to use Naïve Bayes classifier to apply sentiment analysis on the qualitative feedbacks obtained. The outcome of this study is classifying the sentiments of the students’ qualitative feedbacks into positive, neutral and negative and visualizing the sentiment results in a Power BI Dashboard.

Item Type: Final Year Project
Academic Subject : Academic Department - Information Communication Technology
Subject: Q Science > Q Science (General)
Divisions: Sciences and Information Technology > Computer and Information Sciences
Depositing User: Ahmad Suhairi Mohamed Lazim
Date Deposited: 23 Sep 2021 23:44
Last Modified: 23 Sep 2021 23:44
URI: http://utpedia.utp.edu.my/id/eprint/21701

Actions (login required)

View Item View Item

Document Downloads

More statistics for this item...