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Cognitive Computing Approaches for Human Activity Recognition from Tweets—A Case Study of Twitter Marketing Campaign

Part of the Springer Proceedings in Complexity book series (SPCOM)

Abstract

Big data analytics is a growing area of research and has in the recent years introduced many applications for business sector to create value from data. Cognitive computing provides new approaches for analysing social media data, e.g. text analytics and machine vision, available via APIs and software libraries. This study focuses on cognitive computing approaches for recognizing and categorizing valence, arousal and human activity from tweets. The aim is to identify human activities from tweets and map these activities on pleasure and arousal dimensions using cognitive computing services in order to derive new insights for business. A case study of Twitter marketing campaign of a company providing stress management services is presented. In the marketing campaign participants submitted images and text on events that help them to recover from stress. These tweets are analysed with cognitive computing services to recognize activities that participants of the campaign performed to recover from stress and these are compared against qualitative analysis. The caption recognition was successful at detecting some of the activities of the users and overall results indicate that many participants cope with stress by e.g. performing outdoor activities. The two sentiment detection algorithms were also successful in detecting consumer sentiments from the text component of the tweets. Finally, implications for business are drawn from the results of analysis.

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Notes

  1. 1.

    https://pypi.org/project/twitter/.

  2. 2.

    https://azure.microsoft.com/en-us/services/cognitive-services/computer-vision/.

  3. 3.

    http://sentistrength.wlv.ac.uk/.

  4. 4.

    https://github.com/cjhutto/vaderSentiment.

  5. 5.

    https://pypi.org/project/googletrans/.

  6. 6.

    https://www.ibm.com/watson/services/tone-analyzer/.

References

  1. Jalonen, H., Jussila, J.: Developing a Conceptual Model for the Relationship Between Social Media Behavior, Negative Consumer Emotions and Brand Disloyalty. Presented at the (2016)

    Google Scholar 

  2. Magids, S., Zorfas, A., Leemon, D.: The new science of customer emotions. Harv. Bus. Rev. November 2, (2015)

    Google Scholar 

  3. Laros, F., Steenkamp, J.: Emotions in consumer behavior: a hierarchical approach. J. Bus. Res. 58, 1437–1445 (2005)

    CrossRef  Google Scholar 

  4. Zhu, Z., Blanke, U., Calatroni, A., Tröster, G.: Prior knowledge of human activities from social data. In: Proceedings of the 2013 International Symposium on Wearable Computers. pp. 141–142. ACM (2013)

    Google Scholar 

  5. Jussila, J.: Social media in business-to-business companies’ innovation. http://urn.fi/URN:ISBN:978-952-15-3621-2 (2015)

  6. Madhala, P., Jussila, J., Aramo-Immonen, H., Suominen, A.: Systematic literature review on customer emotions in social media. In: ECSM 2018 5th European Conference on Social Media (2018)

    Google Scholar 

  7. Jussila, J., Boedeker, M., Jalonen, H., Helander, N.: Social media analytics empowering customer experience insight. In: Kavoura, A., Sakas, D., Tomaras, P. (eds.) Strategic Innovative Marketing, pp. 25–30. Springer, Cham (2017)

    CrossRef  Google Scholar 

  8. Fosso Wamba, S., Akter, S., Edwards, A., Chopin, G., Gnanzou, D.: How “big data” can make big impact: Findings from a systematic review and a longitudinal case study. Int. J. Prod. Econ. 165, 234–246 (2015)

    CrossRef  Google Scholar 

  9. Vatrapu, R.: Understanding social business. In: Emerging Dimensions of Technology Management. pp. 147–158. Springer India, India (2013)

    Google Scholar 

  10. Jussila, J., Menon, K., Gupta, J., Kärkkäinen, H.: Who is who in big social data? A bibliographic network analysis study. In: Proceedings of The 4th European Conference on Social Media. pp. 161–169. Academic Conferences and publishing limited, Vilnius (2017)

    Google Scholar 

  11. Coté, M.: The materiality of big social data. Cult. Stud. Rev. 20, 121–149 (2014)

    CrossRef  Google Scholar 

  12. Bravo-Marquez, F., Mendoza, M., Poblete, B.: Meta-level sentiment models for big social data analysis. Knowl.-Based Syst. 69, 86–99 (2014)

    CrossRef  Google Scholar 

  13. Batrinca, B., Treleaven, P.C.: Social media analytics: a survey of techniques, tools and platforms. AI Soc. 30, 89–116 (2015)

    CrossRef  Google Scholar 

  14. Bello-Orgaz, G., Jung, J., Camacho, D.: Social big data: Recent achievements and new challenges. Inf. Fusion. 28, 44–59 (2016)

    CrossRef  Google Scholar 

  15. Valiant, L.: Cognitive computation. In: Proceedings of IEEE 36th Annual Foundations of Computer Science. IEEE (1995)

    Google Scholar 

  16. Gutierrez-Garcia, J., López-Neri, E.: Cognitive computing: a brief survey and open research challenges. In: 3rd International Conference on Applied Computing and Information Technology/2nd International Conference on Computational Science and Intelligence. pp. 328–333. IEEE (2015)

    Google Scholar 

  17. Boyd, D., Crawford, K.: Critical Questions for Big Data. Information, Commun. Soc. 15, 662–679 (2012)

    Google Scholar 

  18. Bruns, A.: Faster than the speed of print: reconciling ’big data’social media analysis and academic scholarship. First Monday. 18, (2013)

    Google Scholar 

  19. Davenport, T.H., Prusak, L.: Working knowledge : how organizations manage what they know. Harvard Business School Press (1998)

    Google Scholar 

  20. Buyya, R., Yeo, C., Venugopal, S., Broberg, J., Brandic, I.: Cloud computing and emerging IT platforms: vision, hype, and reality for delivering computing as the 5th utility. Futur. Gener. Comput. Syst. 25, 599–616 (2009)

    CrossRef  Google Scholar 

  21. Ekman, P.: An argument for basic emotions. Cogn. Emot. 6, 169–200 (1992)

    CrossRef  Google Scholar 

  22. Plutchik, R.: Robert: the nature of emotions. Am. Sci. 89, 344 (2001)

    CrossRef  Google Scholar 

  23. Mehrabian, A., Russell, J.: An Approach to Environmental Psychology. The MIT Press, Cambridge (1974)

    Google Scholar 

  24. Seo, M., Barrett, L., Jin, S.: The structure of affect: history, theory, and implications for emotion research in organizations. In: Ashkanasy, N., Cooper, C. (eds.) New Horizons in Management. Research Companion to Emotion in Organizations. pp. 17–44. Edward Elgar Publishing, Northampton (2008)

    Google Scholar 

  25. Scherer, K.R.: What are emotions? And how can they be measured? Soc. Sci. Inf. 44, 695–729 (2005)

    CrossRef  Google Scholar 

  26. Zhao, J., Gou, L., Wang, F., Zhou, M.: PEARL: an interactive visual analytic tool for understanding personal emotion style derived from social media. In: 2014 IEEE Conference on Visual Analytics Science and Technology (VAST). pp. 203–212. IEEE (2014)

    Google Scholar 

  27. Bradley, M., Lang, P.: Affective norms for English words (ANEW): Instruction manual and affective ratings. (1999)

    Google Scholar 

  28. Mehrabian, A.: Basic dimensions for a general psychological theory : implications for personality, social, environmental, and developmental studies. Oelgeschlager, Gunn & Hain (1980)

    Google Scholar 

  29. Russell, J.A.: A circumplex model of affect. J. Pers. Soc. Psychol. 39, 1161–1178 (1980)

    CrossRef  Google Scholar 

  30. Davidson, R.J., Sharon Begley, W.: The emotional life of your brain: how its unique patterns affect the way you think, feel, and live-and how you can change them (2012)

    Google Scholar 

  31. Zimmerman, C., Stein, M.-K., Hardt, D., Vatrapu, R.: Emergence of things felt: harnessing the semantic space of Facebook feeling tags (2015)

    Google Scholar 

  32. Russell, J.: Pancultural aspects of the human conceptual organization of emotions. http://psycnet.apa.org/record/1984–14108-001

  33. Wang, Z., Chong, C.S., Lan, L., Yang, Y., Beng Ho, S., Tong, J.C.: Fine-grained sentiment analysis of social media with emotion sensing. In: 2016 Future Technologies Conference (FTC). pp. 1361–1364. IEEE (2016)

    Google Scholar 

  34. Socher, R., Socher, R., Perelygin, A., Wu, J.Y., Chuang, J., Manning, C.D., Ng, A.Y., Potts, C.: Recursive deep models for semantic compositionality over a sentiment treebank. (2013)

    Google Scholar 

  35. Chafale, D., Pimpalkar, A.: Review on developing corpora for sentiment analysis using Plutchik’s wheel of emotions with fuzzy logic. Int. J. Comput. Sci. Eng. (2014)

    Google Scholar 

  36. Menon, K., Karkkainen, H., Jussila, J., Huhtamaki, J., Mukkamala, R., Lasrado, L., Hussain, A.: Analysing the role of crowdfunding in entrepreneurial ecosystems: a social media event study of two competing product launches. Int. J. Entrep. Small Bus. 33, 575–606 (2018)

    CrossRef  Google Scholar 

  37. Lavidge, R., Steiner, G.: A model for predictive measurements of advertising effectiveness. J. Mark. 59–62 (1961)

    Google Scholar 

  38. Larsen, H.H., Forsberg, J.M., Hemstad, S.V., Mukkamala, R.R., Hussain, A., Vatrapu, R.: TV ratings versus social media engagement: big social data analytics of the Scandinavian TV talk show Skavlan. In: 2016 IEEE International Conference on Big Data (Big Data). pp. 3849–3858. IEEE (2016)

    Google Scholar 

  39. Sarakit, P., Theeramunkong, T., Haruechaiyasak, C., Okumura, M.: Classifying emotion in Thai youtube comments. In: 2015 6th International Conference of Information and Communication Technology for Embedded Systems (IC-ICTES). pp. 1–5. IEEE (2015)

    Google Scholar 

  40. Li, W., Xu, H.: Text-based emotion classification using emotion cause extraction. Expert Syst. Appl. 41, 1742–1749 (2014)

    CrossRef  Google Scholar 

  41. Shukri, S.E., Yaghi, R.I., Aljarah, I., Alsawalqah, H.: Twitter sentiment analysis: a case study in the automotive industry. In: 2015 IEEE Jordan Conference on Applied Electrical Engineering and Computing Technologies (AEECT). pp. 1–5. IEEE (2015)

    Google Scholar 

  42. Wilson, T., Wiebe, J., Hoffmann, P.: Recognizing contextual polarity in phrase-level sentiment analysis. In: Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing—HLT ’05. pp. 347–354. Association for Computational Linguistics, Morristown, NJ, USA (2005)

    Google Scholar 

  43. Strapparava, C., Valitutti, A.: WordNet-Affect: an affective extension of WordNet. In: Proceedings of the International Conference on Language Resources and Evaluation (2004)

    Google Scholar 

  44. Sun, X., Zhang, C., Li, G., Sun, D., Ren, F., Zomaya, A., Ranjan, R.: Detecting users’ anomalous emotion using social media for business intelligence. J. Comput. Sci. (2017)

    Google Scholar 

  45. Xu, A., Liu, Z., Guo, Y., Sinha, V., Akkiraju, R.: A new chatbot for customer service on social media. In: Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems—CHI ’17, pp. 3506–3510. ACM Press, New York, USA (2017)

    Google Scholar 

  46. Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Vanderplas, J.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011)

    Google Scholar 

  47. Kristina, S., Shelley, K.: Developing the American time use survey activity classification system. Mon. Labor Rev. 3, (2005)

    Google Scholar 

  48. Zhu, Z., Blanke, U., Calatroni, A., Tröster, G.: Human activity recognition using social media data. In: Proceedings of the 12th International Conference on Mobile and Ubiquitous Multimedia. p. 21 (2013)

    Google Scholar 

  49. Dearman, D., Sohn, T., Truong, K.: Opportunities exist: continuous discovery of places to perform activities. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. pp. 2429–2438. ACM (2011)

    Google Scholar 

  50. Beber, M.: Individual and group activity recognition in moving object trajectories (2017)

    Google Scholar 

  51. Saunders, M.N.K., Lewis, P., Thornhill, A.: Research methods for business students. Prentice Hall (2009)

    Google Scholar 

  52. Yin, R.K.: Case study research : design and methods. Sage Publications (2003)

    Google Scholar 

  53. Cowley, B.U., Torniainen, J.: A short review and primer on electrodermal activity in human computer interaction applications (2016)

    Google Scholar 

  54. Bradley, M., Lang, P.: Emotion and motivation (2000)

    Google Scholar 

  55. Jussila, J., Venho, N., Salonius, H., Moilanen, J., Liukkonen, J., Rinnetmäki, M.: Towards ecosystem for research and development of electrodermal activity applications. In: Proceedings of the 22nd International Academic Mindtrek Conference. pp. 79–87. ACM (2018)

    Google Scholar 

  56. Thelwall, M.: The Heart and soul of the web? Sentiment strength detection in the social web with SentiStrength. In: Holyst, J. (ed.) Cyberemotions. pp. 119–134. Springer (2017)

    Google Scholar 

  57. Hutto, C.J., Gilbert, E.: Vader: a parsimonious rule-based model for sentiment analysis of social media text. ICWSM. (2014)

    Google Scholar 

  58. Oinas-Kukkonen, H.: Personalization Myopia: a viewpoint to true personalization of information systems. In: Proceedings of the 22nd International Academic Mindtrek Conference. pp. 88–91. ACM (2018)

    Google Scholar 

  59. Bernabé-Moreno, J., Tejeda-Lorente, A., Porcel, C., Fujita, H., Herrera-Viedma, E.: Emotional profiling of locations based on social media. Procedia Comput. Sci. 55, 960–969 (2015)

    CrossRef  Google Scholar 

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Jussila, J., Madhala, P. (2019). Cognitive Computing Approaches for Human Activity Recognition from Tweets—A Case Study of Twitter Marketing Campaign. In: Visvizi, A., Lytras, M. (eds) Research & Innovation Forum 2019. RIIFORUM 2019. Springer Proceedings in Complexity. Springer, Cham. https://doi.org/10.1007/978-3-030-30809-4_15

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  • DOI: https://doi.org/10.1007/978-3-030-30809-4_15

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