Social Data Analysis of Brazilian’s Mood from Twitter

Prata, David Nadler; Soares, Kleber P.; Silva, Michel A.; Trevisan, Daniela Q.; Letouze, Patrick

doi:10.7763/ijssh.2016.v6.640

Cited by 11 publications

(10 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Researchers increasingly note the promise of computational approaches to “open new doors” for health research [ 21 , 81 ]. Studies using large-scale computational text analysis have previously found that emotional words map onto experienced emotion and reveal new patterns beyond those reported in self-report survey studies, but this approach is rarely used in weight management research [ 25 , 26 , 27 , 28 , 29 , 30 , 31 , 82 , 83 ]. We extended this work to weight management by collecting over 1,573,000 words used in goal setting and striving conversations on a mobile weight loss program.…”

Section: Discussionmentioning

confidence: 99%

“…Computational approaches through digital technologies such as mobile weight loss programs make possible new insights that are difficult or impossible to collect otherwise [ 21 , 22 , 23 ]. One particularly relevant large area of research analyzes large amounts of naturally occurring language data in order to understand changes in individuals’ emotions over time [ 24 , 25 , 26 , 27 , 28 , 29 ]. For example, previous work has gathered user-generated language from social media and has demonstrated that emotional words change over time.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

How Do Emotions during Goal Pursuit in Weight Change over Time? Retrospective Computational Text Analysis of Goal Setting and Striving Conversations with a Coach during a Mobile Weight Loss Program

Behr

Mitchell

et al. 2021

IJERPH

View full text Add to dashboard Cite

During behavioral weight management, individuals reflect on their progress and barriers through goal pursuit (goal setting and goal striving). Emotions during goal pursuit are largely unknown, and previous investigations of emotions in weight management have primarily relied on self-report. In this retrospective study, we used a well-validated computational text analysis approach to explore how emotion words changed over time during goal setting and striving conversations with a coach in a mobile weight loss program. Linear mixed models examined changes in emotion words each month from baseline to program end and compared emotion words between individuals who set an overall concrete goal for the program (concrete goal setters) and those who set an overall abstract goal (abstract goal setters). Contrary to findings using self-report, positive emotion words were stable and negative emotion words significantly increased over time. There was a marginal trend towards greater negative emotion word use being associated with greater weight loss. Concrete goal setters used more positive words than abstract goal setters, with no differences in negative emotion words and weight loss. Implications include the possibility that individuals may need increasing support over time for negative emotions expressed during goal setting and striving, and concrete goals could boost positive emotion. Future research should investigate these possibilities.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

How Do Emotions during Goal Pursuit in Weight Change over Time? Retrospective Computational Text Analysis of Goal Setting and Striving Conversations with a Coach during a Mobile Weight Loss Program

Behr

Mitchell

et al. 2021

IJERPH

View full text Add to dashboard Cite

show abstract

“…Through the week, weekends were much happier than weekdays. Identified patterns were confirmed by the study of Brazilians' Mood from Twitter [214], where Naive Bayes [30] was applied to classify sentiment. Dzogang also explored circadian patterns in mood changes [215] While for many languages, such studies have already been conducted, the research of Russian-language content remains quite limited [93], [137].…”

Section: Future Research Opportunitiesmentioning

confidence: 92%

The Applications of Sentiment Analysis for Russian Language Texts: Current Challenges and Future Perspectives

Smetanin

2020

IEEE Access

View full text Add to dashboard Cite

Sentiment analysis has become a powerful tool in processing and analysing expressed opinions on a large scale. While the application of sentiment analysis on English-language content has been widely examined, the applications on the Russian language remains not as well-studied. In this survey, we comprehensively reviewed the applications of sentiment analysis of Russian-language content and identified current challenges and future research directions. In contrast with previous surveys, we targeted the applications of sentiment analysis rather than existing sentiment analysis approaches and their classification quality. We synthesised and systematically characterised existing applied sentiment analysis studies by their source of analysed data, purpose, employed sentiment analysis approach, and primary outcomes and limitations. We presented a research agenda to improve the quality of the applied sentiment analysis studies and to expand the existing research base to new directions. Additionally, to help scholars selecting an appropriate training dataset, we performed an additional literature review and identified publicly available sentiment datasets of Russian-language texts. INDEX TERMS Classification, machine learning, computational linguistics, sentiment analysis, applications of sentiment analysis, Russian-language texts, public opinion.

show abstract

“…A study comparing three learning algorithms (Naıve Bayes, SVM and MaxEnt) and three feature selection methods (Chi-Square, CPD and CPPD) for classifying texts related to 2014 elections in Brazil is presented in [12] . The authors analyzed and visualized Twitter messages, ranking the posts relatively to variations in moods within the Brazilian territory.…”

Section: Size Of Convolution Filtersmentioning

confidence: 99%

Sentiment Analysis of Tweets in Brazilian Portuguese with Convolutional Neural Networks

Coello

Neto

2019

Int J Innov Educ Res

View full text Add to dashboard Cite

Sentiment analysis of texts posted on Twitter is a natural language processing task whose importance has grown along with the increase in the number of users of the platform and the interest of organizations on the opinions of their employees, customers and users.Although Brazil is the sixth country in the world with most active users of Tweeter and Portuguese is the seventh most spoken language in the world, with 221 million speakers (200 million of them living in Brazil), the number of articles that discuss sentiment analysis approaches for Brazilian Portuguese is a small fraction of those that focus on the English language. On the other hand, few works use deep learning for this task when compared with other machine learning and lexical based methods. In this context, the work described in this article addresses the problem using Convolutional Neural Networks (CNN). The paper presents the results of an experimental evaluation that shows that a CNN with a relatively simple architecture can perform much better than a previous approach that uses ensembles of other machine learning classifiers combined with text preprocessing heuristics

show abstract

Social Data Analysis of Brazilian’s Mood from Twitter

Cited by 11 publications

References 7 publications

How Do Emotions during Goal Pursuit in Weight Change over Time? Retrospective Computational Text Analysis of Goal Setting and Striving Conversations with a Coach during a Mobile Weight Loss Program

How Do Emotions during Goal Pursuit in Weight Change over Time? Retrospective Computational Text Analysis of Goal Setting and Striving Conversations with a Coach during a Mobile Weight Loss Program

The Applications of Sentiment Analysis for Russian Language Texts: Current Challenges and Future Perspectives

Sentiment Analysis of Tweets in Brazilian Portuguese with Convolutional Neural Networks

Contact Info

Product

Resources

About