MARC details
| 000 -LEADER |
|---|
| fixed length control field |
03562nam a2200337 i 4500 |
| 003 - CONTROL NUMBER IDENTIFIER |
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| control field |
MY-KuUP |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
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| control field |
20251125111038.0 |
| 006 - FIXED-LENGTH DATA ELEMENTS--ADDITIONAL MATERIAL CHARACTERISTICS |
|---|
| fixed length control field |
t||||fs|||| 000 0 |
| 007 - PHYSICAL DESCRIPTION FIXED FIELD--GENERAL INFORMATION |
|---|
| fixed length control field |
ta |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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| fixed length control field |
250506t20232023my a|||fs|||| 000 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
|---|
| International Standard Book Number |
THE0010040 (Local) |
| Qualifying information |
Hardback |
| 040 ## - CATALOGING SOURCE |
|---|
| Original cataloging agency |
UMPSA |
| Language of cataloging |
eng |
| Transcribing agency |
UMPSA |
| 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 .A379 2023 r Bc |
| 100 0# - MAIN ENTRY--PERSONAL NAME |
|---|
| Personal name |
Siti Nur Aisyah Binti Anuar, |
| Relator term |
author. |
| 245 10 - TITLE STATEMENT |
|---|
| Title |
Identifying potential customers using graph social media analytics / |
| Statement of responsibility, etc. |
Siti Nur Aisyah Binti Anuar |
| 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 |
xii , 81 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 Honour -- Universiti Malaysia Pahang – 2023 |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE |
|---|
| Bibliography, etc. note |
Includes bibliographical references |
| 520 3# - SUMMARY, ETC. |
|---|
| Summary, etc. |
In today's digital era, businesses try to find new clients in many ways. This study offers approaches that use graph social media analytics to identify potential clients for the product Soft Pinch Liquid Blush. The dataset used in this study was gathered from Twitter search queries and contains parameters such as date, time, username identification, text, language, hashtags, reply count, retweet count, like count, quote count, discussion identification, retweet tweet, quoted tweet, reply identification, user and location. The main objective of this study is to discover potential customers for the product Soft Pinch Liquid Blush using graph social media analytics, as well as find significant sources and nodes relevant to the product. The analysis starts by building a graph network with the Source and Target columns, where the Source column represents people who tweeted about the product and the Target column represents people who were referenced or reacted to a tweet, indicating prospective interest. The number of likes, retweets and sentiment values are then used to generate influence scores. Voterank and centrality metrics are also used to evaluate node significance. To find communities in the network, three graph community techniques which are Louvain, Clauset-Newman-Moore greedy modularity and label propagation are used. The community results were compared using modularity, coverage and performance. Based on the result, node “SGomezNewsCOMsNode” has the highest influence score for both the number of likes and retweets by considering sentiment value. Node “rarebeauty” was the most influential node based on voterank and centrality metrics. Louvain and Clauset-Newman-Moore algorithms appear to outperform label propagation in terms of modularity and performance score of community structure. However, label propagation outperforms Louvain and Clauset-Newman-Moore algorithms in terms of coverage. The study concludes that Louvain and Clauset-Newman-Moore are the suitable algorithms in this study, even though the modularity value was low. For future research, it recommended to add more edges between nodes to increase the overall connection of the network and do network pre-processing such as noise removal, sparsity reduction and edge weight normalization to enhance the modularity score. |
| 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 Report |
| General subdivision |
Dissertations |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
|---|
| Source of classification or shelving scheme |
Library of Congress Classification |
| Koha item type |
Final Year Report |