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020			_aTHE0010040 (Local) _qHardback
040			_aUMPSA _beng _cUMPSA _erda
090			_aPSM .A379 2023 r Bc
100	0		_aSiti Nur Aisyah Binti Anuar, _eauthor.
245	1	0	_aIdentifying potential customers using graph social media analytics / _cSiti Nur Aisyah Binti Anuar
264		1	_aKuantan Pahang : _bUMPSA, _c2023
264		4	_c© 2023
300			_axii , 81 pages : _bIllustration (some colour) ; _e1 CD-ROM.
336			_2rdacontent _atext
337			_2rdamedia _aunmediated
338			_2rdacarrier _avolume
347			_2rda _atext file _bPDF
500			_aCenter for Mathematical Sciences
502			_aBachelor of Applied Science In Data Analytics With Honour -- Universiti Malaysia Pahang – 2023
504			_aIncludes bibliographical references
520	3		_aIn 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	2	0	_aCenter for Mathematical Sciences _xDissertations
650		0	_aUniversities and colleges _xDissertations
650		0	_aFinal Year Report _xDissertations
942			_2lcc _cPSM