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UID:pretalx-foss4g-asia-2024-TLBAPD@talks.geoinfo-lab.org
DTSTART;TZID=+07:20241217T113000
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DESCRIPTION:This study explores the integration of Natural Language Process
 ing (NLP) and Geographic Information Systems (GIS) to analyze the spatial 
 distribution and sentiment of restaurants based on TikTok data. Data was c
 ollected from TikTok using primary and secondary hashtags related to resta
 urant reviews in Bangkok. The resulting database enabled a detailed analys
 is of restaurant locations and customer sentiment using Logistic Regressio
 n for sentiment analysis. The findings indicate that negative reviews were
  predicted with the highest accuracy (84%)\, followed by positive (78%) an
 d neutral (76%) reviews. The spatial analysis identified a dense of restau
 rants in the inner districts of Bangkok. This integration of NLP and GIS n
 ot only mapped the popularity of restaurants as mentioned on TikTok but al
 so provided significant insights into consumer behavior and preferences. T
 he study demonstrates the effectiveness of combining NLP and GIS for geosp
 atial analysis\, offering a powerful tool for understanding social media t
 rends and their impact on local businesses. The results underscore the pot
 ential for leveraging social media data to inform urban planning and busin
 ess strategies\, particularly in the context of food and hospitality indus
 tries.
DTSTAMP:20260315T075745Z
LOCATION:Auditorium Hall 2
SUMMARY:Social Media Data analysis in a Restaurant Context : A Case Study o
 f TikTok - Asamaporn Sitthi
URL:https://talks.geoinfo-lab.org/foss4g-asia-2024/talk/TLBAPD/
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