Social Network Mining Algorithm for Generating User Persona on Facebook.
| Autor: | LEE, Connie, 李依恬 |
|---|---|
| Rok vydání: | 2019 |
| Druh dokumentu: | 學位論文 ; thesis |
| Popis: | 107 Living in a rapid growth world with the easy accessible social media, the way of how people shared their opinions has been changed. Human being shared their ideas, songs, videos, knowledges and digital contents on social media by stepping on this fast-growing world. Business and profits generated by where people gathered or the way people made purchased. In this point of view, corporate started to think of how marketing channel should be shifted from Off-line advertising to digital advertising. Giant firms changed their marketing strategy by aiming every single iconic digital user’s profiles. Focus on their personal can helped organization get closer to target customers. Past studies mostly investigated by comparing inside sales data with customer’s uniqueness. The Internet of things world, every single user left unique digital shadow after they browsed social media. This digital shadow was positively related to targeting potential customers in terms of both effectively and accuracy. This research aims to reveal the relation between Facebook’s fan page from a Car manufacturer with all active followers from June/2017 to Dec/2017. The researcher focused on highly activated followers (GS). By tracking users interact with other fan pages as our target fan page (TP) and find out the post they interact. After text mining (jieba) we established the post- keywords matrix R. Through Social Network Mining Algorithm for Generating User Persona (SNAUP). The SNAUP could depiction GS's user persona. This analysis observed all activity monthly from followers to run the experiment. Firstly, the outcomes from July to September GS were discussed on consumers, dedicated lines and stores. However, these keywords:” Video” and “Flagship” were discussed from October to December which is different from previous three month. This experiment could obviously be showing that the topic and keyword will normally change quarterly. |
| Databáze: | Networked Digital Library of Theses & Dissertations |
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