Death remembered by family, death recorded by reporter : A semantic network analysis and topic modeling of the text memorializing Sewol ferry victims

2021 ◽  
Vol 65 (6) ◽  
pp. 482-518
Author(s):  
WanSoo Lee ◽  
Myungil Choi
Keyword(s):  
Network Analysis ◽  
Topic Modeling ◽  
Semantic Network ◽  
Semantic Network Analysis

scholarly journals Exploring halal tourism tweets on social media

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Ali Feizollah ◽  
Mohamed M. Mostafa ◽  
Ainin Sulaiman ◽  
Zalina Zakaria ◽  
Ahmad Firdaus
Keyword(s):  
Social Media ◽  
Network Analysis ◽  
Topic Modeling ◽  
Semantic Network ◽  
Word List ◽  
Global Market ◽  
Semantic Network Analysis ◽  
Tourist Destinations ◽  
Muslim Countries ◽  
Emotion Lexicon

AbstractThis study explores tweets from Oct 2008 to Oct 2018 related to halal tourism. The tweets were extracted from twitter and underwent various cleaning processes. A total of 33,880 tweets were used for analysis. Analysis intended to (1) identify the topics users tweet about regarding halal tourism, and (2) analyze the emotion-based sentiment of the tweets. To identify and analyze the topics, the study used a word list, concordance graphs, semantic network analysis, and topic-modeling approaches. The NRC emotion lexicon was used to examine the sentiment of the tweets. The analysis illustrated that the word “halal” occurred in the highest number of tweets and was primarily associated with the words “food” and “hotel”. It was also observed that non-Muslim countries such as Japan and Thailand appear to be popular as halal tourist destinations. Sentiment analysis found that there were more positive than negative sentiments among the tweets. The findings have shown that halal tourism is a global market and not only restricted to Muslim countries. Thus, industry players should take the opportunity to use social media to their advantage to promote their halal tourism packages as it is an effective method of communication in this decade.

scholarly journals Fashion informatics of the Big 4 Fashion Weeks using topic modeling and sentiment analysis

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Yeong-Hyeon Choi ◽  
Seungjoo Yoon ◽  
Bin Xuan ◽  
Sang-Yong Tom Lee ◽  
Kyu-Hye Lee
Keyword(s):  
New York ◽  
Social Media ◽  
Network Analysis ◽  
Sentiment Analysis ◽  
Topic Modeling ◽  
Semantic Network ◽  
Semantic Network Analysis ◽  
Social Media Data ◽  
Big 4 ◽  
Media Data

AbstractThis study used several informatics techniques to analyze consumer-driven social media data from four cities (Paris, Milan, New York, and London) during the 2019 Fall/Winter (F/W) Fashion Week. Analyzing keywords using a semantic network analysis method revealed the main characteristics of the collections, celebrities, influencers, fashion items, fashion brands, and designers connected with the four fashion weeks. Using topic modeling and a sentiment analysis, this study confirmed that brands that embodied similar themes in terms of topics and had positive sentimental reactions were also most frequently mentioned by the consumers. A semantic network analysis of the tweets showed that social media, influencers, fashion brands, designers, and words related to sustainability and ethics were mentioned in all four cities. In our topic modeling, the classification of the keywords into three topics based on the brand collection’s themes provided the most accurate model. To identify the sentimental evaluation of brands participating in the 2019 F/W Fashion Week, we analyzed the consumers’ sentiments through positive, neutral, and negative reactions. This quantitative analysis of consumer-generated social media data through this study provides insight into useful information enabling fashion brands to improve their marketing strategies.

scholarly journals We Care About Different Things: Non-Elite Conceptualizations of Social Media Privacy

2019 ◽  
Vol 5 (3) ◽  
pp. 205630511986600
Author(s):  
Kelly Quinn ◽  
Dmitry Epstein ◽  
Brenda Moon
Keyword(s):  
Social Media ◽  
Network Analysis ◽  
Topic Modeling ◽  
Representative Sample ◽  
Research Policy ◽  
Semantic Network ◽  
Information Access ◽  
Semantic Network Analysis ◽  
Privacy Concerns ◽  
Unique Dataset

This study explores privacy from the perspective of the user. It leverages a “framing in thought” approach to capture how users make sense of privacy in their social media use. It builds on a unique dataset of privacy definitions collected from a representative sample of 608 US social media users. The data are analyzed using topic modeling and semantic network analysis to unpack the multidimensionality of social media privacy. These dimensions are further examined in relation to established demographic antecedents of privacy concerns and behaviors. Results indicate the dominance of frames related to horizontal dimensions of privacy, or privacy vis-à-vis peers, as compared with the vertical dimensions, or privacy vis-à-vis institutions. In addition, the findings suggest that user conceptualization of privacy reflects a cognate-based approach that emphasizes control and limits to information access. Implications for privacy research, policy, and technology design are discussed.

Sign in / Sign up

Export Citation Format

Share Document