scholarly journals Toward human-centric urban infrastructure: Text mining for social media data to identify the public perception of COVID-19 policy in transportation hubs

2021 ◽  
pp. 103524
Author(s):  
June Young Park ◽  
Evan Mistur ◽  
Donghwan Kim ◽  
Yunjeong Mo ◽  
Richard Hoefer
Keyword(s):  
Social Media ◽  
Text Mining ◽  
Public Perception ◽  
Urban Infrastructure ◽  
Social Media Data ◽  
The Public ◽  
Media Data

scholarly journals Social Media Analytics: Data Utilization of Social Media for Research

2019 ◽  
Vol 1 (2) ◽  
pp. 193-205
Author(s):  
Ria Andryani ◽  
Edi Surya Negara ◽  
Dendi Triadi
Keyword(s):  
Social Media ◽  
Public Perception ◽  
Data Retrieval ◽  
Media Analysis ◽  
Internet Technology ◽  
Mining Machine ◽  
Social Media Analytics ◽  
Social Media Data ◽  
Visualization Techniques ◽  
Media Data

The amount of production data generated by social media opportunities that can be exploited by various parties, both government and private sectors to produce the information. Social media data can be used to know the behavior and public perception of the phenomenon or a particular event. To obtain and analyze social media data needed depth knowledge of Internet technology, social media, databases, data structures, information theory, data mining, machine learning, until the data and information visualization techniques. In this research, social media analysis on a particular topic and the development of prototype devices software used as a tool of social media data retrieval or retrieval of data applications. Social Media Analytics (SMA) aims to make the process of analysis and synthesis of social media data to produce information can be used by those in need. SMA process is done in three stages, namely: Capture, Understand and Present. This research is exploratorily focused on understanding the technology that became the basis of social media using various techniques exist and is already used in the study of social media analytic previously.

Using social media data in diabetes care: Bridging the conceptual gap between health providers and patients (Preprint)

2021 ◽  
Author(s):  
Ru-Hsueh Wang ◽  
Yu-Wen Hong ◽  
Chia-Chun Li ◽  
Siao-Ling Li ◽  
Jenn-Long Liu ◽  
...  
Keyword(s):  
Social Media ◽  
Chinese Medicine ◽  
Sexual Dysfunction ◽  
Text Mining ◽  
Real World ◽  
Diabetes Care ◽  
Weight Reduction ◽  
Health Providers ◽  
Social Media Data ◽  
Media Data

BACKGROUND Diabetic patients with poor education about the disease may exhibit poor compliance and thus subsequently experience more complications. However, the conceptual gap between the diabetes education provided by health providers and the non-compliance of patients is still not well understood in the real world. OBJECTIVE Disclosing what people think about diabetes on social media may help to close this gap. METHODS In this study, social media data was collected from the OpView social media platform. After checking the quality of the data, we analyzed the trends in people’s discussions on the Internet using text mining. The natural language process, including word segmentation, and word count, and counting the relationships between the words. A word cloud is developed, and a clustering analyses are also performed. RESULTS There were 19,565 posts about diabetes collected from forums, community websites, and Q&A websites in 2017. The three most popular aspects of diabetes were diet (33.2%), life adjustment (21.2%), and avoiding complications (15.6%). Most of the discussions about diabetes were negative, and the top three negative ratios aspects were avoiding complications (7.60), problem-solving (4.08) and exercise (3.97). In terms of diet, the most popular topics were Chinese medicine and special diet therapy. In terms of life adjustment, financial issues, weight reduction, and a less painful glucometer were discussed the most. Furthermore, sexual dysfunction, neuropathy, nephropathy, and retinopathy were the most worrying issues in the avoiding complications area. Using text mining, we found that people care most about sexual dysfunction. Health providers care about the benefits of exercise in diabetes care, but people are mostly really concerned about sexual functioning. CONCLUSIONS A conceptual gap between health providers and diabetes patients existed in this real-world social media investigation. To spread healthy diabetic education concepts in the media, health providers might wish to provide more information related to patients actual areas of concern, such as sexual function, Chinese medicine, and weight reduction.

scholarly journals A Comprehensive Analysis of Approaches for Sentiment Analysis Using Twitter Data on COVID-19 Vaccines

2021 ◽  
Vol 2 (2) ◽  
pp. 1-10
Author(s):  
Amrita Mishra ◽  
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Sentiment Analysis ◽  
Text Classification ◽  
Comprehensive Analysis ◽  
Social Media Data ◽  
The Public ◽  
Opinion Analysis ◽  
Twitter Data ◽  
Media Data

Sentiment Analysis has paved routes for opinion analysis of masses over unrestricted territorial limits. With the advent and growth of social media like Twitter, Facebook, WhatsApp, Snapchat in today’s world, stakeholders and the public often takes to expressing their opinion on them and drawing conclusions. While these social media data are extremely informative and well connected, the major challenge lies in incorporating efficient Text Classification strategies which not only overcomes the unstructured and humongous nature of data but also generates correct polarity of opinions (i.e. positive, negative, and neutral). This paper is a thorough effort to provide a brief study about various approaches to SA including Machine Learning, Lexicon Based, and Automatic Approaches. The paper also highlights the comparison of positive, negative, and neutral tweets of the Sputnik V, Moderna, and Covaxin vaccines used for preventive and emergency use of COVID-19 disease.

Exploring Public Perceptions of Native-Born American Emigration Abroad and Renunciation of American Citizenship through Social Media

2019 ◽  
pp. 60-101
Author(s):  
Shalin Hai-Jew
Keyword(s):  
Social Media ◽  
Research Literature ◽  
Social Phenomena ◽  
Social Media Data ◽  
The Public ◽  
The Social ◽  
American Citizenship ◽  
Social Media Platforms ◽  
Work Done ◽  
Media Data

There has been little work done on American emigration abroad and even less done on the formal renunciation of American citizenship. This chapter provides an overview of both phenomena in the research literature and then provides some methods for using the extraction of social media data and their visualization as a way of tapping into the public mindsets about these social phenomena. The software tools used include the following: Network Overview, Discovery and Exploration for Excel (NodeXL Basic), NVivo, and Maltego Carbon; the social media platforms used include the following: Wikipedia, YouTube, Twitter, and Flickr.

Applying sentiment analytics to examine social media crises: a case study of United Airline's crisis in 2017

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Xin Tian ◽  
Wu He ◽  
Feng-Kwei Wang
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Social Media Data ◽  
Content Type ◽  
The Public ◽  
Response Strategies ◽  
Related Information ◽  
Valence And Arousal ◽  
Public Reaction ◽  
Media Data

PurposeIn recent years, social media crises occurred more and more often, which negatively affect the reputations of individuals, businesses and communities. During each crisis, numerous users either participated in online discussion or widely spread crisis-related information to their friends and followers on social media. By applying sentiment analysis to study a social media crisis of airline carriers, the purpose of this research is to help companies take measure against social media crises.Design/methodology/approachThis study used sentiment analytics to examine a social media crisis related to airline carriers. The arousal, valence, negative, positive and eight emotional sentiments were applied to analyze social media data collected from Twitter.FindingsThis research study found that social media sentiment analysis is useful to monitor public reaction after a social media crisis arises. The sentiment results are able to reflect the development of social media crises quite well. Proper and timely response strategies to a crisis can mitigate the crisis through effective communication with the customers and the public.Originality/valueThis study used the Affective Norms of English Words (ANEW) dictionary to classify the words in social media data and assigned the words with two elements to measure the emotions: valence and arousal. The intensity of the sentiment determines the public reaction to a social media crisis. An opinion-oriented information system is proposed as a solution for resolving a social media crisis in the paper.

Social media analytics: how they support company public relations

2019 ◽  
Vol 40 (1) ◽  
pp. 28-34 ◽  
Author(s):  
Lisa Tam ◽  
Jeong-Nam Kim
Keyword(s):  
Social Media ◽  
Public Relations ◽  
Design Methodology ◽  
Social Media Analytics ◽  
Social Media Data ◽  
Content Type ◽  
The Public ◽  
The Public Sphere ◽  
Use Of Social Media ◽  
Media Data

Purpose In the midst of practitioners’ increasing use of social media analytics (SMA) in guiding public relations (PR) strategy, this paper aims to present the capabilities and limitations of these tools and offers suggestions on how to best use them to gain research-based insights. Design/methodology/approach This review assesses the capabilities and limitations of SMA tools based on industry reports and research articles on trends in PR and SMA. Findings The strengths of SMA tools lie in their capability to gather and aggregate a large quantity of real-time social media data, use algorithms to analyze the data and present the results in ways meaningful to organizations and understand networks of issues and publics. However, there are also challenges, including the increasing restricted access to social media data, the increased use of bots, skewing social conversations in the public sphere, the lack of capability to analyze certain types of data, such as visual data and the discrepancy between data collected on social media and through other methods. Originality/value This review suggests that PR professionals acknowledge the capabilities and limitations of SMA tools when using them to inform strategy.

scholarly journals Social Media Data Analytics to Enhance Sustainable Communications between Public Users and Providers in Weather Forecast Service Industry

2020 ◽  
Vol 12 (20) ◽  
pp. 8528
Author(s):  
Ki-Kwang Lee ◽  
In-Gyum Kim
Keyword(s):  
Social Media ◽  
Textual Analysis ◽  
Service Industry ◽  
Weather Forecast ◽  
Forecast Errors ◽  
Social Media Data ◽  
The Public ◽  
Rain Events ◽  
Industry Needs ◽  
Media Data

The weather forecast service industry needs to understand customers’ opinions of the weather forecast to enhance sustainable communication between forecast providers and recipients particularly influenced by inherent uncertainty in the forecast itself and cultural factors. This study aims to investigate the potential for using social media data to analyze users’ opinions of the wrong weather forecast. Twitter data from Korea in 2014 are analyzed using textual analysis and association rule mining to extract meaningful emotions or behaviors from weather forecast users. The results of textual analysis show that the frequency of negative opinions is considerably high compared to positive opinions. More than half of the tweets mention precipitation forecasts among the meteorological phenomena, implying that most Koreans are sensitive to rain events. Moreover, association rules extracted from the negative tweets reveal a pattern of user criticism according to the seasons and types of forecast errors such as a “false alarm” or “miss” error. This study shows that social media data can provide valuable information on the actual opinion of the forecast users in almost real time, enabling the weather forecast providers to communicate effectively with the public.

Discovering Next Generation Product Innovations by Identifying Lead User Preferences Expressed Through Large Scale Social Media Data

2014 ◽  
Author(s):  
Suppawong Tuarob ◽  
Conrad S. Tucker
Keyword(s):  
Social Media ◽  
Large Scale ◽  
User Preferences ◽  
Next Generation ◽  
Social Media Data ◽  
The Public ◽  
Lead User ◽  
Product Features ◽  
Latent Features ◽  
Media Data

An innovative consumer (a.k.a. a lead user) is a consumer of a product that faces needs unknown to the public. Innovative consumers play important roles in the product development process as their ideas tend to be innovatively unique and can be potentially useful for development of next generation, innovative products that better satisfy the market needs. Oftentimes, consumers portray their usage experience and opinions about products and product features through social networks such as Twitter and Facebook, making social media a viable, rich in information, and large-scale source for mining product related information. The authors of this work propose a data mining methodology to automatically identify innovative consumers from a heterogeneous pool of social media users. Specifically, a mathematical model is proposed to identify latent features (product features unknown to the public) from social media data. These latent features then serve as the key to discover innovative users from the ever increasing pool of social media users. A real-world case study, which identifies smartphone lead users in the pool of Twitter users, illustrates promising success of the proposed models.

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