MARC details
| 000 -LEADER |
|---|
| fixed length control field |
02677ntm a2200337 i 4500 |
| 003 - CONTROL NUMBER IDENTIFIER |
|---|
| control field |
MY-KuUP |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
|---|
| control field |
20251125110901.0 |
| 006 - FIXED-LENGTH DATA ELEMENTS--ADDITIONAL MATERIAL CHARACTERISTICS |
|---|
| fixed length control field |
t||||fr|||| 000 0 |
| 007 - PHYSICAL DESCRIPTION FIXED FIELD--GENERAL INFORMATION |
|---|
| fixed length control field |
ta |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
|---|
| fixed length control field |
240530t20232023my a|||| |||| 00| 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
|---|
| International Standard Book Number |
THE0009862 (Local) |
| Qualifying information |
Hardback |
| 040 ## - CATALOGING SOURCE |
|---|
| Original cataloging agency |
UMPSA |
| Language of cataloging |
eng |
| Transcribing agency |
UMP |
| Description conventions |
rda |
| 090 ## - LOCALLY ASSIGNED LC-TYPE CALL NUMBER (OCLC); LOCAL CALL NUMBER (RLIN) |
|---|
| Classification number (OCLC) (R) ; Classification number, CALL (RLIN) (NR) |
PSM .F38 2023 r Bc |
| 100 1# - MAIN ENTRY--PERSONAL NAME |
|---|
| Personal name |
Fatin Farhanah Binti Abd Rahim, |
| Relator term |
author. |
| 245 10 - TITLE STATEMENT |
|---|
| Title |
Sentiment analysis on food reviews in kuantan, pahang using machine learning / |
| Remainder of title |
Fatin Farhanah Binti Abd Rahim |
| 264 #1 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE |
|---|
| Place of production, publication, distribution, manufacture |
Kuantan, Pahang: |
| Name of producer, publisher, distributor, manufacturer |
UMPSA, |
| Date of production, publication, distribution, manufacture, or copyright notice |
2023 |
| 264 #4 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE |
|---|
| Date of production, publication, distribution, manufacture, or copyright notice |
2023 |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Extent |
xiii, 77 pages : |
| Other physical details |
Illustration (some colour) ; |
| Accompanying material |
1 CD-ROM. |
| 336 ## - CONTENT TYPE |
|---|
| Source |
rdacontent |
| Content type term |
text |
| 337 ## - MEDIA TYPE |
|---|
| Source |
rdamedia |
| Media type term |
unmediated |
| 338 ## - CARRIER TYPE |
|---|
| Source |
rdacarrier |
| Carrier type term |
volume |
| 347 ## - DIGITAL FILE CHARACTERISTICS |
|---|
| Source |
rda |
| File type |
text file |
| Encoding format |
PDF |
| 500 ## - GENERAL NOTE |
|---|
| General note |
Center for Mathematical Sciences |
| 502 ## - DISSERTATION NOTE |
|---|
| Dissertation note |
Bachelor of Applied Science in Data Analytics with Honours--Universiti Malaysia Pahang – 2023 |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE |
|---|
| Bibliography, etc. note |
Includes bibliographical reference |
| 520 3# - SUMMARY, ETC. |
|---|
| Summary, etc. |
Customer sentiment analysis is an automated way detecting sentiments in online interactions in order to assess customer opinions about a product, brand or service. It assists companies in gaining insights and efficiently responding to their customers. Social media, blogs and websites have become channels for people to voice their opinions openly on a variety discussion topics making it ideal domain to utilise customers sentiment analysis. This study presents a machine learning approach to analyse how sentiment analysis detects positive and negative feedback about food reviews in Kuantan, Pahang. Customer feedback data will be taken from the suitable social media. All of these data processes will use Python via Jupyter Notebook. These data will be go through a pre-processing stage and the data will be fed to two supervised learning algorithms consisting of Support Vector Machine (SVM) and Logistic Regression. The performance of each model will be compared to select the most accurate model. At the end, the objectives of the project can be achieved because the sentiment analysis is necessary to collect and analyse the large amount of data to give benefit to the customers and producer to discover a psotive or negative reviews. Among the benefits is that the restaurant can make such improvements such as can upgrade existing menu based on customers feedback and customers can choose which is the best food that suits their tastes |
| 610 20 - SUBJECT ADDED ENTRY--CORPORATE NAME |
|---|
| Corporate name or jurisdiction name as entry element |
Center for Mathematical Sciences |
| General subdivision |
Dissertations |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Universities and colleges |
| General subdivision |
Dissertations |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Final Year Project |
| General subdivision |
Dissertations |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
|---|
| Source of classification or shelving scheme |
Library of Congress Classification |
| Koha item type |
Final Year Report |