scholarly journals One More Tweet: Firms Challenge a Sustainable Future

2020 ◽  
Vol 5 (2) ◽  
pp. 201-219
Author(s):  
Marco Tregua ◽  
Anna D'Auria
Keyword(s):  
Content Analysis ◽  
Text Mining ◽  
Sentiment Analysis ◽  
Semantic Analysis ◽  
Global Community ◽  
Huge Amount ◽  
Double Step ◽  
Research Context ◽  
Online Sources ◽  
Share Information

In the last decades, researchers have been provided with a huge amount of data thanks to the diffusion of online sources. Additionally, more companies are issuing reports and documents to share information with stakeholders about their sustainable approach to both strengthen and encourage people to adopt a similar approach. To support researchers in managing the increasing quantity of information, several tools have been provided for text mining studies, such as sentiment analysis, semantic analysis, and content analysis. We proposed to analyse the usefulness of automated and semi-automated techniques on a dataset composed of more than 875,000 tweets posted by the companies that Forbes (2020) considers to be the most sustainable. We chose to focus on sustainability because it is a topic of interest to the global community, as revealed by the significant amount of attention that companies are paying to it. In detail, we performed a double-step analysis: firstly, a comparison between exact words and stemmed words; secondly, a description of communication efforts and topics that firms opted for when dealing with sustainability. Our expected contribution is mainly methodological, as we provide suggestions regarding the advantages of performing the analysis in one of the two ways, while the research context offers insights into sustainability

Support for Cyberbullying Victims and Actors

2019 ◽  
Vol 10 (2) ◽  
pp. 35-56 ◽  
Author(s):  
Sophia Alim ◽  
Shehla Khalid
Keyword(s):  
Content Analysis ◽  
Sentiment Analysis ◽  
Automated Extraction ◽  
Parts Of Speech ◽  
Pos Tagging ◽  
Personal Experiences ◽  
Group Members ◽  
Share Information

This study analyses the post content and the emotions reflected in 10 open Facebook groups associated with cyberbullying, with the highest number of group members. Automated extraction via Facebook API was used to gather the data. Altogether, 313 Facebook posts were extracted and coded for content analysis. Sentiment analysis and parts of speech (POS) tagging was used to explore the emotions reflected in the content. The study findings revealed that (1) the content of the posts was mainly opinion-based in comparison to expressing personal experiences of cyberbullying. This indicated Facebook groups require stronger moderation due to digression of topics discussed. (2) Only 3% of posts in this study contained advice about cyberbullying. (3) Sentiment analysis of the posts showed that the Facebook groups focused on cyberbullying, reflected more positive sentiments in their posts. This is encouraging to cyberbullying victims to share information on cyberbullying. The findings in this study lay the foundations for more research into support for cyberbullying victims.

scholarly journals The #orthorexia community on Instagram

2021 ◽  
Author(s):  
Martina Valente ◽  
Sophie Renckens ◽  
Joske Bunders-Aelen ◽  
Elena V. Syurina
Keyword(s):  
Content Analysis ◽  
Sense Of Belonging ◽  
Mixed Methods Study ◽  
Therapeutic Interventions ◽  
Negative Consequences ◽  
Online Questionnaire ◽  
Level Of Evidence ◽  
Helping Others ◽  
The Relationship ◽  
Share Information

Abstract Purpose This mixed-methods study delved into the relationship between orthorexia nervosa (ON) and Instagram. Methods Two quantitative data sources were used: content analysis of pictures using #orthorexia (n = 3027), and an online questionnaire investigating the experience of ON and the use of Instagram of people sharing ON-related content on Instagram (n = 185). Following, interviews (n = 9) were conducted with people posting ON-related content on Instagram and self-identifying as having (had) ON. Results People who share ON-related content on Instagram were found to be primarily young women (questionnaire = 95.2% females, mean age 26.2 years; interviews = 100% females, mean age 28.4 years), who were found to be heavy social media users and favor Instagram over other platforms. Questionnaire respondents agreed in defining ON as an obsession with a diet considered healthy, with bio-psycho-social negative consequences, though those who self-identified as having (had) ON were more likely to point out the negative impairments of ON. Interviewees deemed Instagram partially responsible for the development of ON. Instead, they agreed that Instagram encourages problem realization. Content analysis showed that ON is encoded in pictures of ‘food’, ‘people’, ‘text’ and ‘other.’ Interviewees revealed that they started posting to recover, share information, help others, and they felt inspired to post by other accounts. A sense of belonging to the #orthorexia community emerged, where people share values and ideals, and seek validation from others. Conclusion Conversations around #orthorexia on Instagram generate supportive communities aiding recovery. Individuals use Instagram for helping others and themselves recovering from ON. Understanding how people help each other, manage their health, cope with symptoms, and undertake recovery can inform the implementation of therapeutic interventions for ON. Level of evidence Level III, evidence obtained from well-designed cohort or case–control analytic studies.

Sentiment Analysis with Text Mining in Contexts of Big Data

2017 ◽  
Vol 13 (3) ◽  
pp. 47-67 ◽  
Author(s):  
Carina Sofia Andrade ◽  
Maribel Yasmina Santos
Keyword(s):  
Big Data ◽  
Text Mining ◽  
Sentiment Analysis ◽  
Decision Making Process ◽  
Practical Applications ◽  
Information Models ◽  
Evolution Of Technology ◽  
The Common ◽  
Analyse Data

The evolution of technology, along with the common use of different devices connected to the Internet, provides a vast growth in the volume and variety of data that are daily generated at high velocity, phenomenon commonly denominated as Big Data. Related with this, several Text Mining techniques make possible the extraction of useful insights from that data, benefiting the decision-making process across multiple areas, using the information, models, patterns or tendencies that these techniques are able to identify. With Sentiment Analysis, it is possible to understand which sentiments and opinions are implicit in this data. This paper proposes an architecture for Sentiment Analysis that uses data from the Twitter, which is able to collect, store, process and analyse data on a real-time fashion. To demonstrate its utility, practical applications are developed using real world examples where Sentiment Analysis brings benefits when applied. With the presented demonstration case, it is possible to verify the role of each used technology and the techniques adopted for Sentiment Analysis.

scholarly journals A Survey on Analysis of Twitter Opinion Mining Using Sentiment Analysis

2019 ◽  
pp. 537-542
Author(s):  
Vishnu VardanReddy ◽  
Mahesh Maila ◽  
Sai Sri Raghava ◽  
Yashwanth Avvaru ◽  
Sri. V. Koteswarao
Keyword(s):  
Social Networking ◽  
Sentiment Analysis ◽  
Social Networking Sites ◽  
Opinion Mining ◽  
Online Communication ◽  
Valuable Insight ◽  
Huge Amount ◽  
Twitter Data ◽  
Product Service ◽  
Different Types

In recent years, there is a rapid growth in online communication. There are many social networking sites and related mobile applications, and some more are still emerging. Huge amount of data is generated by these sites everyday and this data can be used as a source for various analysis purposes. Twitter is one of the most popular networking sites with millions of users. There are users with different views and varieties of reviews in the form of tweets are generated by them. Nowadays Opinion Mining has become an emerging topic of research due to lot of opinionated data available on Blogs & social networking sites. Tracking different types of opinions & summarizing them can provide valuable insight to different types of opinions to users who use Social networking sites to get reviews about any product, service or any topic. Analysis of opinions & its classification on the basis of polarity (positive, negative, neutral) is a challenging task. Lot of work has been done on sentiment analysis of twitter data and lot needs to be done. In this paper we discuss the levels, approaches of sentiment analysis, sentiment analysis of twitter data, existing tools available for sentiment analysis and the steps involved for same. Two approaches are discussed with an example which works on machine learning and lexicon based respectively.

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