State Legislators’ Divergent Social Media Response to the Opioid Epidemic from 2014 to 2019: Longitudinal Topic Modeling Analysis

2021 ◽  
Author(s):  
Daniel C. Stokes ◽  
Jonathan Purtle ◽  
Zachary F. Meisel ◽  
Anish K. Agarwal
Keyword(s):  
Social Media ◽  
Topic Modeling ◽  
State Legislators ◽  
Opioid Epidemic ◽  
Modeling Analysis

scholarly journals Topic Modeling Analysis of Social Enterprises: Twitter Evidence

2020 ◽  
Vol 12 (8) ◽  
pp. 3419 ◽  
Author(s):  
Shr-Wei Kao ◽  
Pin Luarn
Keyword(s):  
Social Media ◽  
Topic Modeling ◽  
Application Programming Interface ◽  
Social Enterprises ◽  
Four Dimensions ◽  
Modeling Analysis ◽  
Application Programming ◽  
Rich Information ◽  
Programming Interface

Social media is a major channel used for communication by professional and social groups. The text posted on social media contains extremely rich information. To capture the development of social enterprises (SEs), this paper examines the tweets posted on Twitter and searches the hashtags on the Twitter Application Programming Interface (API) that SEs deem to be the most important. The results suggest that these tweets can be divided into three content groups (strategy, impact and business). This paper expands this into four dimensions (strategy, impact, business and people) and six indicators (social, opportunity, change, enterprise, network and team) and establishes a conceptual framework of SEs. This paper aims to enhance the understanding of the pertinent issues recently affecting SEs and extract findings that can act as a reference for follow-up studies.

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 A Bilingual Comparison of Sentiment and Topics for a Product Event on Twitter

2021 ◽  
Author(s):  
Irina Wedel ◽  
Michael Palk ◽  
Stefan Voß
Keyword(s):  
Social Media ◽  
Language Processing ◽  
Topic Modeling ◽  
New Product ◽  
Business Value ◽  
Data Driven ◽  
New Product Introduction ◽  
Social Media Analytics ◽  
Product Introduction ◽  
Textual Data

AbstractSocial media enable companies to assess consumers’ opinions, complaints and needs. The systematic and data-driven analysis of social media to generate business value is summarized under the term Social Media Analytics which includes statistical, network-based and language-based approaches. We focus on textual data and investigate which conversation topics arise during the time of a new product introduction on Twitter and how the overall sentiment is during and after the event. The analysis via Natural Language Processing tools is conducted in two languages and four different countries, such that cultural differences in the tonality and customer needs can be identified for the product. Different methods of sentiment analysis and topic modeling are compared to identify the usability in social media and in the respective languages English and German. Furthermore, we illustrate the importance of preprocessing steps when applying these methods and identify relevant product insights.

A topic modeling analysis of Korea’s T&I research trends in the 2010s

Babel ◽  
2021 ◽  
Author(s):  
Changsoo Lee
Keyword(s):  
Topic Modeling ◽  
Research Output ◽  
Research Trends ◽  
Korean Language ◽  
Modeling Analysis ◽  
Trends Over Time ◽  
New Research ◽  
Topic Structure ◽  
Manual Classification

Abstract The present study aims to demonstrate the relevance of topic modeling as a new research tool for analyzing research trends in the T&I field. Until now, most efforts to this end have relied on manual classification based on pre-established typologies. This method is time- and labor-consuming, prone to subjective biases, and limited in describing a vast amount of research output. As a key component of text mining, topic modeling offers an efficient way of summarizing topic structure and trends over time in a collection of documents while being able to describe the entire system without having to rely on sampling. As a case study, the present paper applies the technique to analyzing a collection of abstracts from four Korean Language T&I journals for the 2010s decade (from 2010 to 2019). The analysis proves the technique to be highly successful in uncovering hidden topical structure and trends in the abstract corpus. The results are discussed along with implications of the technique for the T&I field.

Social Media Communication about HPV Vaccine in China: A Study Using Topic Modeling and Survey

2021 ◽  
pp. 1-12
Author(s):  
Shaohai Jiang ◽  
Pianpian Wang ◽  
Piper Liping Liu ◽  
Annabel Ngien ◽  
Xingtong Wu
Keyword(s):  
Social Media ◽  
Hpv Vaccine ◽  
Topic Modeling ◽  
Media Communication ◽  
Social Media Communication
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