The synergic relationship between e-commerce and Sentiment Analysis: A content analysis of published articles between 2007 and 2020

2020 ◽  
Author(s):  
M. A. Casas-Valadez ◽  
A. Faz-Mendoza ◽  
C. E. Medina-Rodriguez ◽  
R. Castaneda-Miranda ◽  
N. K. Gamboa-Rosales ◽  
...  
Keyword(s):  
Content Analysis ◽  
Sentiment Analysis

Analysis of Tweets Related to Cyberbullying

2015 ◽  
Vol 5 (4) ◽  
pp. 31-52 ◽  
Author(s):  
Sophia Alim
Keyword(s):  
Content Analysis ◽  
Young People ◽  
Sentiment Analysis ◽  
News Events

The use of Twitter, especially by teenagers and young people, has raised the issue of cyberbullying. There is a lack of research into what types of advice and support are available in tweets for cyberbullying victims, and into the features influencing the spread of tweets related to cyberbullying. In this study, 7,315 tweets associated with cyberbullying were extracted and analysed. The results highlighted that tweets containing features such as a higher number of URLs, hashtags, or followers did not necessarily lead to a higher number of retweets. Sentiment analysis of the tweets presented both positive and negative sentiments from users towards cyberbullying. This study manually sampled 400 tweets for content analysis. Tweets covered a variety of areas associated with cyberbullying ranging from user opinions to news events. Results showed that 33% of tweets contained advice and support for cyberbullying victims. These tweets produced the highest number of retweets in comparison with tweets covering other areas associated with cyberbullying.

The “Blue Check” Communication on the Pandemia Vaccine Campaign

2022 ◽  
pp. 664-685
Author(s):  
Domenico Trezza ◽  
Miriam Di Lisio
Keyword(s):  
Content Analysis ◽  
Sentiment Analysis ◽  
Topic Modeling ◽  
Critical Issue ◽  
The Public ◽  
Opinion Analysis ◽  
Attitudes And Perceptions

This chapter has the exploratory goal of understanding the attitudes and perceptions of 'verified' Twitter (VA) accounts about the COVID-19 vaccine campaign. Identifying their sentiment and opinion about it could therefore be crucial to the success of vaccination. A content analysis of tweets from the period December 24, 2020 to March 23, 2021 about the vaccine campaign in Italy was conducted to understand the semantic strategies used by VAs based on their orientation toward the vaccine, whether pro, anti, or neutral, and their possible motivations. Topic modeling allowed the authors to detect five prevalent themes and their associated words. A sentiment analysis and opinion analysis were performed on a smaller sample of tweets. The results suggest that 'authoritative' opinion about the vaccine has been very fragmented and not entirely positive, as expected. This could prove to be a critical issue in getting the vaccine positively accepted by the public.

scholarly journals Social Media Data-Based Sentiment Analysis of Tourists’ Air Quality Perceptions

2019 ◽  
Vol 11 (18) ◽  
pp. 5070 ◽  
Author(s):  
Yuguo Tao ◽  
Feng Zhang ◽  
Chunyun Shi ◽  
Yun Chen
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Content Analysis ◽  
Air Quality ◽  
Sentiment Analysis ◽  
Social Media Data ◽  
Sina Weibo ◽  
Emotion Words ◽  
Tourist Destinations ◽  
Media Data

Analyzing tourists’ perceptions of air quality is of great significance to the study of tourist experience satisfaction and the image construction of tourism destinations. In this study, using the web crawler technique, we collected 27,500 comments regarding the air quality of 195 of China’s Class 5A tourist destinations posted by tourists on Sina Weibo from January 2011 to December 2017; these comments were then subjected to a content analysis using the Gooseeker, ROST CM (Content Mining System) and BosonNLP (Natural Language Processing) tools. Based on an analysis of the proportions of sentences with different emotional polarities with ROST EA (Emotion Analysis), we measured the sentiment value of texts using the artificial neural network (ANN) machine learning method implemented through a Chinese social media data-oriented Boson platform based on the Python programming language. The content analysis results indicated that in the adaption stage in Sina Weibo, tourists’ perceptions of air quality were mainly positive and had poor air pollution crisis awareness. Objective emotion words exhibited a similarly high proportion as subjective emotion words, indicating that taking both objective and subjective emotion words into account simultaneously helps to comprehensively understand the emotional content of the comments. The sentiment analysis results showed that for the entire text, sentences with positive emotions accounted for 85.53% of the total comments, with a sentiment value of 0.786, which belonged to the positive medium level; the direction of the temporal “up-down-up” changes and the spatial pattern of high in the south and low in the north (while having little difference between the east and the west) were basically consistent with reality. A further exploration of the theoretical basis of the semi-supervised ANN approach or the introduction of other machine learning methods using different data sources will help to analyze this phenomenon in greater depth. The paper provides evidence for new data and methods for air quality research in tourist destinations and provides a new tool for air quality monitoring.

scholarly journals Breastfeeding Media Coverage and Beliefs During the COVID-19 Pandemic in Mexico: Implications for Breastfeeding Equity.

2021 ◽  
Author(s):  
Mireya Vilar-Compte ◽  
Pablo Gaitán-Rossi ◽  
Elizabeth C. Rhodes ◽  
Valeria Cruz-Villaba ◽  
R. Pérez-Escamilla
Keyword(s):  
Content Analysis ◽  
Sentiment Analysis ◽  
Media Coverage ◽  
Swot Analysis ◽  
Descriptive Analysis ◽  
Breastfeeding Promotion ◽  
Mexican Government ◽  
The Media ◽  
Households With Children ◽  
Negative Messages

Abstract Background: Breastfeeding offers short- and long- term health benefits to mothers and children and constitutes a priority for public health. Evidence shows that SARS-CoV-2 is not likely to be transmitted via breastmilk. Moreover, antibodies against SARS-CoV-2 are presumably contained in breastmilk of mothers with history of COVID-19 infection or vaccination. Direct breastfeeding is the preferred infant feeding option during the pandemic, but conflicting practices have been adopted, which could widen existing disparities in breastfeeding. This study aims to describe how was information about breastfeeding communicated in Mexican media during the pandemic and assess Mexican adults’ beliefs regarding breastfeeding among mothers infected with COVID-19.Methods: A retrospective content analysis of media coverage on breastfeeding in Mexico between March 1 and September 24, 2020, excluding advertisements, was done. For the content analysis, both a sentiment analysis and an analysis based on strengths, weaknesses, opportunities and threats for breastfeeding promotion were performed. Also, we incorporated a descriptive analysis from the July 2020 wave of the ENCOVID-19 survey, which included questions on beliefs about breastfeeding. This information was stratified by gender, age, and socioeconomic status.Results: 1014 publications on breastfeeding were identified in internet, newspapers, TV, and magazines. Most information was published during World Breastfeeding Week, celebrated in August. Based on the sentiment analysis, 57.2% of all information was classified as positive, and based on the SWOT analysis, most information was classified either as strengths or opportunities for breastfeeding promotion. However, the ENCOVID-19 data showed that 67.3% of people living in households with children under 3 years of age believe that mothers with COVID-19 should not breastfeed, and 19.8% stated that they simply didn’t know. These beliefs showed differences both by gender and by socioeconomic status.Conclusions: While the Mexican government endorsed the recommendations on breastfeeding during the COVID-19 pandemic, communication of those messages was sporadic, inconstant and unequal across types of media. Moreover, there were also negative messages for breastfeeding circulating on the media. There continues to be a widespread notion that mothers with COVID-19 should not breastfeed and, due to differences on beliefs by socioeconomic status, health inequities could be exacerbated.

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

scholarly journals Automated vs Manual Content Analysis – A Retrospective Look

2020 ◽  
Vol 67 (SI) ◽  
pp. 57-67
Author(s):  
Ionuț-Daniel Anastasiei ◽  
◽  
Mircea Radu Georgescu ◽  
◽  
Keyword(s):  
Qualitative Research ◽  
Social Networks ◽  
Content Analysis ◽  
Qualitative Analysis ◽  
Sentiment Analysis ◽  
User Generated Content

Content Analysis, which is a part of qualitative analysis, has mainly been studied in scientific articles from health and medicine domains. With the emerge of social networks, there are new opportunities for content analysis, which can be used to analyse user generated content, from various sources. Nevertheless, the companies are investing millions of dollars in content analysis, which is often known as sentiment analysis. The discussion in this article helps to understand the main concepts of content analysis for those interested in the domain of qualitative analysis, with the help of automated and manual qualitative research. The overall conclusion is that automated qualitative analysis is dependent on how accurate is the tool used and this feature can be checked with the help of manual qualitative analysis.

scholarly journals Haters vs. lovers on Facebook

2021 ◽  
Vol 12 (1) ◽  
pp. 27-48
Author(s):  
Milica Vučković
Keyword(s):  
Content Analysis ◽  
Sentiment Analysis ◽  
Response Rate ◽  
Barack Obama ◽  
Top Down ◽  
Negative Sentiment

This paper tries to answer what is the dominant sentiment of comments that users leave on the Facebook fan pages of politicians in power. To answer this question, first the auto-code sentiment analysis of nearly 44,000 comments posted on the Facebook fan page of former US president Barack Obama was conducted. Secondly, content analysis was conducted on 2,411 comments posted on former Croatian president Ivo Josipović’s Facebook fan page. The results of auto-code sentiment analysis showed that examined comments in Obama’s case were mostly neutral and positive, while negative sentiment was the least represented in Obama’s case. The results of content analysis in the Croatian case revealed that the dominant sentiment of all comments was also positive. Finally, it was revealed that the response rate in both cases was zero, what tells us that Obama and Josipović used Facebook only for top-down communication, while the interactive potential of Facebook was neglected.

Using a sentiment analysis for the examination of tourism blogs – a step by step methodological reflection process

2021 ◽  
Vol 13 (2) ◽  
pp. 167-190
Author(s):  
Gero Szepannek ◽  
Laila Westphal ◽  
Werner Gronau ◽  
Tine Lehmann
Keyword(s):  
Content Analysis ◽  
Sentiment Analysis ◽  
Computational Methods ◽  
Research Question ◽  
The Other ◽  
Data Sets ◽  
New Approach ◽  
Other Hand ◽  
The One ◽  
The Given

Abstract The article at hand is driven by a methodological interest in the opportunities and challenges of applying an automated text mining approach, particularly a sentiment analysis on various tourism blogs at the same time. The study aims to answer the question to what extent advanced computational methods can improve the data acquisition and analysis of unstructured data sets stemming from various blogs and forums. Furthermore, the authors intend to explore to what extent the sentiment analysis is able to objectify the qualitative results identified by an earlier analysis by the authors using content analysis done by thematic coding. For the purpose of the specific tourism research question in this paper a new approach is proposed, which consists of a combination of sentiment analyses, supervised learning, and dimensionality reduction in order to identify terms that strongly load on specific emotions. The contribution indicates on the one hand, that advanced computational methods have their own specific constraints, but on the other hand, are able to provide a richer and deeper analysis following a quantitative approach. Several issues have to be taken into account, such as data protection constraints, the need for data cleaning, such as word stemming, dimension reduction, such as removal of custom stop words, and the development of descent ontologies. On the other hand, the quantitative method also provides, due to its standardised procedure, a less subjective insight in the given content, but is not less time consuming than traditional content analysis.

scholarly journals Sentiment/tone (Automated Content Analysis)

10.34778/1d ◽  
2021 ◽  
Author(s):  
Valerie Hase
Keyword(s):  
Machine Learning ◽  
Content Analysis ◽  
Sentiment Analysis ◽  
Supervised Machine Learning ◽  
Data Sets ◽  
Financial News ◽  
State Of The Union ◽  
Automated Content Analysis ◽  
Course Material ◽  
Newspaper Articles

Sentiment/tone describes the way issues or specific actors are described in coverage. Many analyses differentiate between negative, neutral/balanced or positive sentiment/tone as broader categories, but analyses might also measure expressions of incivility, fear, or happiness, for example, as more granular types of sentiment/tone. Analyses can detect sentiment/tone in full texts (e.g., general sentiment in financial news) or concerning specific issues (e.g., specific sentiment towards the stock market in financial news or a specific actor). The datasets referred to in the table are described in the following paragraph: Puschmann (2019) uses four data sets to demonstrate how sentiment/tone may be analyzed by the computer. Using Sherlock Holmes stories (18th century, N = 12), tweets (2016, N = 18,826), Swiss newspaper articles (2007-2012, N = 21,280), and debate transcripts (2013-2017, N = 205,584), he illustrates how dictionaries may be applied for such a task. Rauh (2019) uses three data sets to validate his organic German language dictionary for sentiment/tone. His data consists of sentences from German parliament speeches (1991-2013, N = 1,500), German-language quasi-sentences from German, Austrian and Swiss party manifestos (1998-2013, N = 14,008) and newspaper, journal and news wire articles (2011-2012, N = 4,038). Silge and Robinson (2020) use six Jane Austen novels to demonstrate how dictionaries may be used for sentiment analysis. Van Atteveldt and Welbers (2020) use state of the Union speeches (1789-2017, N = 58) for the same purpose. The same authors (van Atteveldt & Welbers, 2019) show based on a dataset of N = 2,000 movie reviews how supervised machine learning might also do the trick. In their Quanteda tutorials, Watanabe and Müller (2019) demonstrate the use of dictionaries and supervised machine learning for sentiment analysis on UK newspaper articles (2012-2016, N = 6,000) as well as the same set of movie reviews (n = 2,000). Lastly, Wiedemann and Niekler (2017) use state of the Union speeches (1790-2017, N = 233) to demonstrate how sentiment/tone can be coded automatically via a dictionary approach. Field of application/theoretical foundation: Related to theories of “Framing” and “Bias” in coverage, many analyses are concerned with the way the news evaluates and interprets specific issues and actors. References/combination with other methods of data collection: Manual coding is needed for many automated analyses, including the ones concerned with sentiment. Studies for example use manual content analysis to develop dictionaries, to create training sets on which algorithms used for automated classification are trained, or to validate the results of automated analyses (Song et al., 2020).   Table 1. Measurement of “Sentiment/Tone” using automated content analysis. Author(s) Sample Procedure Formal validity check with manual coding as benchmark* Code Puschmann (2019) (a) Sherlock Holmes stories (b) Tweets (c) Swiss newspaper articles (d) German Parliament transcripts   Dictionary approach Not reported http://inhaltsanalyse-mit-r.de/sentiment.html Rauh (2018) (a) Bundestag speeches (b) Quasi-sentences from German, Austrian and Swiss party manifestos (c) Newspapers, journals, agency reports Dictionary approach Reported https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/BKBXWD Silge & Robinson (2020) Books by Jane Austen Dictionary approach Not reported https://www.tidytextmining.com/sentiment.html van Atteveldt & Welbers (2020) State of the Union speeches Dictionary approach Reported https://github.com/ccs-amsterdam/r-course-material/blob/master/tutorials/sentiment_analysis.md van Atteveldt & Welbers (2019) Movie reviews Supervised Machine Learning Approach Reported https://github.com/ccs-amsterdam/r-course-material/blob/master/tutorials/r_text_ml.md Watanabe & Müller (2019) Newspaper articles Dictionary approach Not reported https://tutorials.quanteda.io/advanced-operations/targeted-dictionary-analysis/ Watanabe & Müller (2019) Movie reviews Supervised Machine Learning Approach Reported https://tutorials.quanteda.io/machine-learning/nb/ Wiedemann & Niekler (2017) State of the Union speeches Dictionary approach Not reported https://tm4ss.github.io/docs/Tutorial_3_Frequency.html *Please note that many of the sources listed here are tutorials on how to conducted automated analyses – and therefore not focused on the validation of results. Readers should simply read this column as an indication in terms of which sources they can refer to if they are interested in the validation of results. References Puschmann, C. (2019). Automatisierte Inhaltsanalyse mit R. Retrieved from http://inhaltsanalyse-mit-r.de/index.html Rauh, C. (2018). Validating a sentiment dictionary for German political language—A workbench note. Journal of Information Technology & Politics, 15(4), 319–343. doi:10.1080/19331681.2018.1485608 Silge, J., & Robinson, D. (2020). Text mining with R. A tidy approach. Retrieved from https://www.tidytextmining.com/ Song, H., Tolochko, P., Eberl, J.-M., Eisele, O., Greussing, E., Heidenreich, T., Lind, F., Galyga, S., & Boomgaarden, H.G. (2020) In validations we trust? The impact of imperfect human annotations as a gold standard on the quality of validation of automated content analysis. Political Communication, 37(4), 550-572. van Atteveldt, W., & Welbers, K. (2019). Supervised Text Classification. Retrieved from https://github.com/ccs-amsterdam/r-course-material/blob/master/tutorials/r_text_ml.md van Atteveldt, W., & Welbers, K. (2020). Supervised Sentiment Analysis in R. Retrieved from https://github.com/ccs-amsterdam/r-course-material/blob/master/tutorials/sentiment_analysis.md Watanabe, K., & Müller, S. (2019). Quanteda tutorials. Retrieved from https://tutorials.quanteda.io/ Wiedemann, G., Niekler, A. (2017). Hands-on: a five day text mining course for humanists and social scientists in R. Proceedings of the 1st Workshop Teaching NLP for Digital Humanities (Teach4DH@GSCL 2017), Berlin. Retrieved from https://tm4ss.github.io/docs/index.html

scholarly journals WEBJOURNALISM AND SENSE CONSTRUCTION: an analysis of the news about the LGBT population in G1 Tocantins

2020 ◽  
Vol 6 (6) ◽  
pp. a8en
Author(s):  
Alice Agnes Spindola Mota Pinho ◽  
Andréia Fernandes da Silva ◽  
Zeninho Luiz Gasparetto Neto
Keyword(s):  
Content Analysis ◽  
Sentiment Analysis ◽  
Lgbt Community ◽  
The Media ◽  
Lgbt People ◽  
Object Of Study

This article analyzes the journalistic production of news that addresses themes involving the LGBT population (Lesbians, Gays, Bisexuals, Transvestites and Transsexuals). Portal G1 Tocantins was chosen to be the object of study of this work collecting news from the year 2017, considered the year with more deaths of LGBT people compared to the last three years. After submitting the corpus to content analysis and sentiment analysis, the results indicate that the media can assist in the construction of meanings, often negative, about the LGBT community and that there is still a lot to be done in the problematization before the different forms of discrimination, oppression of the LGBT community.

Sign in / Sign up

Export Citation Format

Share Document