원문정보
초록
영어
The development of the 4th industry has increased social media, and the rise of COVID-19 has stimulated non-face-to-face services. People's consumption patterns are also changing a lot due to non-face-to-face services. In this paper, food content keywords are derived through social network-based semantic network analysis, emotions are analyzed, and keywords applied to food recommendation platforms are input. We collected food, influencer, and corona keyword analysis data through Textom. A lot of research has been done through online reviews of existing influencer content. However, there is a lack of research on keyword sentiment analysis provided by influencers rather than consumers and research perspectives. This paper uploads language and topics derived through online reviews of existing publications and subscribers, and goes beyond the limits used in marketing methods. By analyzing keywords that influencers suggest when uploading content, you can apply data that applies them to food recommendation platforms and applications.
목차
1. Introduction
2. Related Work
2.1 Research trends related to unstructured big data related to food content
2.2 Food content research trends in non-face-to-face services and social media
2.3 Food Content Influencers and Sentiment Analysis Research Trends
3. Research Method
3.1 Analysis target and data Collection
3.2 Data analysis
4. Analysis Result
4.1 Analysis of the frequency of keywords in documents related to corona, food content, and influencers
4.2 Centrality and Network visualization of Key Words
4.3 Key word subgroups (CONCOR)
5. Conclusion
Acknowledgement
References