PANAS-t: Uma Escala Psicométrica para Medição de Sentimentos no Twitter
Resumo
Twitter tem se tornado um meio importante de comunicação social, onde usuários postam mensagens sobre tudo. Algumas dessas mensagens expressam informações sobre o estado emocional do usuário, o que pode ser útil no desenvolvimento de aplicações que preveem tendências emotivas de uma população ou simplesmente para melhor entender os efeitos de fenômenos mundiais ou locais no humor das pessoas. Nesse trabalho, adaptamos uma escala psicométrica conhecida como PANAS-x, comumente aplicada em forma de questionário, para medir os sentimentos dos usuários do Twitter sobre uma série de eventos de tema social, político e esportivo. Nossos resultados sugerem que o PANAS-t, nossa versão adaptada do PANAS-x, captura corretamente sentimentos para os eventos analisados
Palavras-chave:
Análise de Sentimentos, Escala Psicométrica, Twitter
Referências
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Diakopoulos, N. A. and Shamma, D. A. (2010). Characterizing debate performance via aggregated twitter sentiment. In Proceedings of the 28th international conference on Human factors in computing systems, CHI ’10, pages 1195–1198.
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Gomide, J., Veloso, A., Jr., W. M., Almeida, V., Benevenuto, F., Ferraz, F., and Teixeira, M. (2011). Dengue surveillance based on a computational model of spatio-temporal locality of twitter. In ACM Web Science Conference (WebSci).
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Kim, B. E. and Gilbert, S. (2009). Detecting sadness in 140 characters: Sentiment analysis and mourning michael jackson on twitter. Web Ecology, 03(August).
Kirkpatrick, M. (2011). There Are Now 155m Tweets Posted Per Day, Triple the Number a Year Ago. [link].
Kulshrestha, J., Kooti, F., Nikravesh, A., and Gummadi, K. P. Geographic Dissection of the Twitter Network. In In Proceedings of the 6th International AAAI Conference on Weblogs and Social Media (ICWSM).
MacAskill, E. (2009). US confirms it asked Twitter to stay open to help Iran protesters. [link].
O’Connor, B., Balasubramanyan, R., Routledge, B. R., and Smith, N. A. (2010). From tweets to polls: Linking text sentiment to public opinion time series. In Proceedings of the International AAAI Conference on Weblogs and Social Media.
Pang, B. and Lee, L. (2008). Opinion mining and sentiment analysis. Foundations and Trends in Information Retrieval, 2(1-2):1–135.
Rao, L. (2010). Twitter Seeing 90 Million Tweets Per Day, 25 Percent Contain Links. [link].
Sakaki, T., Okazaki, M., and Matsuo, Y. (2010). Earthquake shakes twitter users: real-time event detection by social sensors. In Proceedings of the 19th international conference on World wide web, WWW ’10, pages 851–860.
Tumasjan, A., Sprenger, T. O., Sandner, P. G., and Welpe, I. M. (2010). Predicting elections with twitter: What 140 characters reveal about political sentiment. Word Journal Of The International Linguistic Association, pages 178–185.
Watson, D. and Clark, L. (1985). Development and validation of brief measures of positive and negative affect: the panas scales. Journal of Personality and Social Psychology, 54(1):1063–1070.
Bates, C. How michael jackson’s death shut down twitter, brought chaos to google...and ’killed off’ jeff goldblum. [link]. Accessed January, 2012.
Benevenuto, F., Magno, G., Rodrigues, T., and Almeida, V. (2010). Detecting spammers on twitter. In Proceedings of the 7th Annual Collaboration, Electronic messaging, Anti-Abuse and Spam Conference (CEAS).
Bollen, J., Pepe, A., and Mao, H. (2009). Modeling public mood and emotion: Twitter sentiment and socio-economic phenomena. Computer, abs/0911.1:450–453.
Cha, M., Benevenuto, F., Haddadi, H., and Gummadi, K. (2012). The world of connections and information flow in twitter. In IEEE Transactions on Systems, Man and Cybernetics Part A.
Cha, M., Haddadi, H., Benevenuto, F., and Gummadi, K. P. (2010). Measuring User Influence in Twitter: The Million Follower Fallacy. In Proceedings of the Fifth International AAAI Conference on Weblogs and Social Media (ICWSM 2010).
Cheong, M. and Lee, V. C. (2011). A microblogging-based approach to terrorism informatics: Exploration and chronicling civilian sentiment and response to terrorism events via twitter. Information Systems Frontiers, 13:45–59.
Chhabra, S., Aggarwal, A., Benevenuto, F., and Kumaraguru, P. (2011). Phi.sh/$ocial: The phishing landscape through short urls. In Proceedings of the 8th Annual Collaboration, Electronic messaging, Anti-Abuse and Spam Conference (CEAS).
Diakopoulos, N. A. and Shamma, D. A. (2010). Characterizing debate performance via aggregated twitter sentiment. In Proceedings of the 28th international conference on Human factors in computing systems, CHI ’10, pages 1195–1198.
Dodds, P. and Danforth, C. (2010). Measuring the happiness of large-scale written expression: Songs, blogs, and presidents. Journal of Happiness Studies, 11(4):441–456.
Gomide, J., Veloso, A., Jr., W. M., Almeida, V., Benevenuto, F., Ferraz, F., and Teixeira, M. (2011). Dengue surveillance based on a computational model of spatio-temporal locality of twitter. In ACM Web Science Conference (WebSci).
HCD. Confidence levels increase among democrats and independents, decrease among republicans after viewing obama’s press conference. [link]. Accessed January 15, 2012.
Jain, R. (1991). The Art of Computer Systems Performance Analysis: Techniques for Experimental Design, Measurement, Simulation, and Modeling. John Wiley and Sons, INC, 1st edition.
Kim, B. E. and Gilbert, S. (2009). Detecting sadness in 140 characters: Sentiment analysis and mourning michael jackson on twitter. Web Ecology, 03(August).
Kirkpatrick, M. (2011). There Are Now 155m Tweets Posted Per Day, Triple the Number a Year Ago. [link].
Kulshrestha, J., Kooti, F., Nikravesh, A., and Gummadi, K. P. Geographic Dissection of the Twitter Network. In In Proceedings of the 6th International AAAI Conference on Weblogs and Social Media (ICWSM).
MacAskill, E. (2009). US confirms it asked Twitter to stay open to help Iran protesters. [link].
O’Connor, B., Balasubramanyan, R., Routledge, B. R., and Smith, N. A. (2010). From tweets to polls: Linking text sentiment to public opinion time series. In Proceedings of the International AAAI Conference on Weblogs and Social Media.
Pang, B. and Lee, L. (2008). Opinion mining and sentiment analysis. Foundations and Trends in Information Retrieval, 2(1-2):1–135.
Rao, L. (2010). Twitter Seeing 90 Million Tweets Per Day, 25 Percent Contain Links. [link].
Sakaki, T., Okazaki, M., and Matsuo, Y. (2010). Earthquake shakes twitter users: real-time event detection by social sensors. In Proceedings of the 19th international conference on World wide web, WWW ’10, pages 851–860.
Tumasjan, A., Sprenger, T. O., Sandner, P. G., and Welpe, I. M. (2010). Predicting elections with twitter: What 140 characters reveal about political sentiment. Word Journal Of The International Linguistic Association, pages 178–185.
Watson, D. and Clark, L. (1985). Development and validation of brief measures of positive and negative affect: the panas scales. Journal of Personality and Social Psychology, 54(1):1063–1070.
Publicado
17/07/2012
Como Citar
GONÇALVES, Pollyanna; DORES, Wellington; BENEVENUTO, Fabricio.
PANAS-t: Uma Escala Psicométrica para Medição de Sentimentos no Twitter. In: BRAZILIAN WORKSHOP ON SOCIAL NETWORK ANALYSIS AND MINING (BRASNAM), 1. , 2012, Curitiba.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2012
.
p. 153-164.
ISSN 2595-6094.
