03562nam a2200337 i 4500 0 0 lcc 0 6 10000 10000 2025-05-06 0 CD13591 T000003189 2025-05-06 00:00:00 2025-05-06 PSM 102506 102512 MY-KuUP 20251125111038.0 t||||fs|||| 000 0 ta 250506t20232023my a|||fs|||| 000 0 eng d THE0010040 (Local) Hardback UMPSA eng UMPSA rda PSM .A379 2023 r Bc Siti Nur Aisyah Binti Anuar, author. Identifying potential customers using graph social media analytics / Siti Nur Aisyah Binti Anuar Kuantan Pahang : UMPSA, 2023 © 2023 xii , 81 pages : Illustration (some colour) ; 1 CD-ROM. rdacontent text rdamedia unmediated rdacarrier volume rda text file PDF Center for Mathematical Sciences Bachelor of Applied Science In Data Analytics With Honour -- Universiti Malaysia Pahang – 2023 Includes bibliographical references 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. Center for Mathematical Sciences Dissertations Universities and colleges Dissertations Final Year Report Dissertations lcc PSM