Skip to main content
View expanded cover

Software Engineering in the Era of Cloud Computing pp 185–209Cite as

Sentiment Analysis of Twitter Data Through Machine Learning Techniques

Part of the Computer Communications and Networks book series (CCN)

Abstract

Cloud computing is a revolutionary technology for businesses, governments, and citizens. Some examples of Software-as-a-Services (SaaS) of cloud computing are banking apps, e-mail, blog, online news, and social networks. In this chapter, we analyze data sets generated by trending topics on Twitter that emerged from Mexican citizens that interacted during the earthquake of September 19, 2017, using sentiment analysis and supervised learning, based on the Ekman’s six emotional model. We built three classifiers to determine the emotions of tweets that belong to the same topic. The classifiers with the best accuracy for predicting emotions were Naive Bayes and support vector machine. We found that the most frequent predicted emotions were happiness, anger, and sadness; also, that 6.5% of predicted tweets were irrelevant. We provide some recommendations about the use of machine learning techniques in sentiment analysis. Our contribution is the expansion of the emotions range, from three (negative, neutral, positive) to six in order to provide more elements to understand how users interact with social media platforms. Future research will include validation of the method with different data sets and emotions, and the addition of new artificial intelligence techniques to improve accuracy.

Keywords

  • Cloud computing
  • Sentiment analysis
  • Machine learning
  • ML
  • Twitter
  • Naive Bayes
  • Ekman’s model

This is a preview of subscription content, access via your institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-030-33624-0_8
  • Chapter length: 25 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   129.00
Price excludes VAT (USA)
  • ISBN: 978-3-030-33624-0
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   169.99
Price excludes VAT (USA)
Hardcover Book
USD   169.99
Price excludes VAT (USA)
Learn about institutional subscriptions
Fig. 8.1
Fig. 8.2
Fig. 8.3
Fig. 8.4

References

  1. Almarabeh T, Majdalawi YK, Mohammad H (2016) Cloud computing of e-government

    Google Scholar 

  2. Sasikala P (2012) Cloud computing and E-governance: advances, opportunities and challenges. Intl J Cloud Appl Comput (IJCAC) 2(4):32–52

    Google Scholar 

  3. Jadhav B, Patankar A (2018) Opportunities and challenges in integrating cloud computing and big data analytics to e-governance. Int J Comput Appl 180(15):6–11

    Google Scholar 

  4. Lohr S (2012) The age of big data. New York Times, 11 (2012)

    Google Scholar 

  5. Liebowitz J (2001) Knowledge management and its link to artificial intelligence. Expert Syst Appl 20(1):1–6

    CrossRef  Google Scholar 

  6. Fan W, Bifet A (2013) Mining big data: current status, and forecast to the future. ACM SIGKDD Explor Newsl 14(2):1–5

    CrossRef  Google Scholar 

  7. Wu X, Zhu X, Wu GQ, Ding W (2014) Data mining with big data. IEEE Trans Knowl Data Eng 26(1):97–107

    CrossRef  Google Scholar 

  8. Sandoval-Almazán R, Valle-Cruz D (2016) Understanding network links in Twitter: a Mexican case study. In: Proceedings of the 17th international digital government research conference on digital government research. ACM, pp 122–128

    Google Scholar 

  9. Shaikh S, Feldman LB, Barach E, Marzouki Y (2017) Tweet sentiment analysis with pronoun choice reveals online community dynamics in response to crisis events. In: Advances in cross-cultural decision making. Springer, Cham, pp 345–356

    Google Scholar 

  10. Vo BKH, Collier NIGEL (2013) Twitter emotion analysis in earthquake situations. Intl J Comput Linguist Appl 4(1):159–173

    Google Scholar 

  11. Sandoval-Almazan R (2019) Using twitter in political campaigns: The case of the PRI candidate in Mexico. In: Civic engagement and politics: concepts, methodologies, tools, and applications. IGI Global, pp 710–726

    Google Scholar 

  12. Sandoval-Almazán R, Valle-Cruz D (2018) Towards an understanding of Twitter networks: the case of the state of Mexico. First Monday, vol 23, no 4

    Google Scholar 

  13. Sandoval-Almazan R, Valle-Cruz D (2018) Facebook impact and sentiment analysis on political campaigns. In: Proceedings of the 19th annual international conference on digital government research: governance in the data age. ACM, p 56

    Google Scholar 

  14. Chen M, Zhang Y, Li Y, Mao S, Leung VC (2015) EMC: emotion-aware mobile cloud computing in 5G. IEEE Netw 29(2):32–38

    CrossRef  Google Scholar 

  15. Preethi G, Krishna PV, Obaidat MS, Saritha V, Yenduri S (2017) Application of deep learning to sentiment analysis for recommender system on cloud. In 2017 international conference on computer, information and telecommunication systems (CITS). IEEE, pp 93–97

    Google Scholar 

  16. Krishna PV, Misra S, Joshi D, Obaidat MS (2013) Learning automata based sentiment analysis for recommender system on cloud. In: 2013 international conference on computer, information and telecommunication systems (CITS). IEEE, pp 1–5

    Google Scholar 

  17. Assunção MD, Calheiros RN, Bianchi S, Netto MA, Buyya R (2015) Big data computing and clouds: trends and future directions. J Parallel Distrib Comput 79:3–15

    CrossRef  Google Scholar 

  18. Karyotis C, Doctor F, Iqbal R, James A, Chang V (2018) A fuzzy computational model of emotion for cloud based sentiment analysis. Inf Sci 433:448–463

    CrossRef  Google Scholar 

  19. Ali F, Kwak D, Khan P, Islam SR, Kim KH, Kwak KS (2017) Fuzzy ontology-based sentiment analysis of transportation and city feature reviews for safe traveling. Transp Res Part C: Emerg Technol 77:33–48

    CrossRef  Google Scholar 

  20. Sreeja PS, Mahalakshmi GS (2017) Emotion models: a review. Int J Control Theory Appl 10:651–657

    Google Scholar 

  21. Ekman PE, Davidson RJ (1994) The nature of emotion: fundamental questions. Oxford University Press, Oxford

    Google Scholar 

  22. Plutchik R (1965) What is an emotion? J Psychol 61(2):295–303

    CrossRef  Google Scholar 

  23. Munezero MD, Montero CS, Sutinen E, Pajunen J (2014) Are they different? affect, feeling, emotion, sentiment, and opinion detection in text. IEEE Trans Affect Comput 5(2):101–111

    CrossRef  Google Scholar 

  24. Nakamura (1993) Kanjo hyogen jiten [dictionary of emotive expressions]. Tokyodo, Teluk Intan

    Google Scholar 

  25. Yang Y, Jia J, Zhang S, Wu B, Chen Q, Li J et al (2014) How do your friends on social media disclose your emotions? In: Twenty-eighth AAAI conference on artificial intelligence

    Google Scholar 

  26. Wang Y, Pal A (2015) Detecting emotions in social media: a constrained optimization approach. In: Twenty-fourth international joint conference on artificial intelligence

    Google Scholar 

  27. Cambria E, Livingstone A, Hussain A (2012) The hourglass of emotions. In Cognitive behavioural systems. Springer, Berlin, pp 144–157

    CrossRef  Google Scholar 

  28. Zhang L, Hua K, Wang H, Qian G, Zhang L (2014) Sentiment analysis on reviews of mobile users. Procedia Comput Sci 34:458–465

    CrossRef  Google Scholar 

  29. Zhang L, Hua K, Wang H, Qian G, Zhang L (2014) Sentiment analysis on reviews of mobile users. Procedia Comput Sci 34:458–465

    CrossRef  Google Scholar 

  30. Prabowo R, Thelwall M (2009) Sentiment analysis: a combined approach. J Informetr 3(2):143–157

    CrossRef  Google Scholar 

  31. Kumar M, Bala A (2016) Analyzing Twitter sentiments through big data. In: 2016 3rd international conference on computing for sustainable global development (INDIACom). IEEE, pp 2628–2631

    Google Scholar 

  32. Subramaniyaswamy V, Vijayakumar V, Logesh R, Indragandhi V (2015) Unstructured data analysis on big data using map reduce. Procedia Comput Sci 50:456–465

    CrossRef  Google Scholar 

  33. Al-Kabi M, Al-Ayyoub M, Alsmadi I, Wahsheh H (2016) A prototype for a standard arabic sentiment analysis corpus. Int Arab J Inf Technol 13(1A):163–170

    Google Scholar 

  34. Kune R, Konugurthi PK, Agarwal A, Chillarige RR, Buyya R (2016) The anatomy of big data computing. Softw Pract Exp 46(1):79–105

    Google Scholar 

  35. Sidorov G et al (2013) Empirical study of machine learning based approach for opinion mining in tweets. In: Batyrshin I, González Mendoza M (eds) Advances in artificial intelligence. MICAI 2012. Lecture notes in computer science. Springer, Berlin, vol 7629

    Google Scholar 

  36. Chawla NV, Bowyer KW, Hall LO, Kegelmeyer WP (2002) SMOTE: synthetic minority over-sampling technique. J Artif Int Res AI Access Found 16:321–357

    CrossRef  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Autonomous University of the State of Mexico, 55600, Valle Hermoso, Zumpango, Estado de México, Mexico

    Asdrúbal López-Chau

  2. Autonomous University of the State of Mexico, Instituto Literario # 100, Toluca, Mexico

    David Valle-Cruz & Rodrigo Sandoval-Almazán

Authors
  1. Asdrúbal López-Chau
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. David Valle-Cruz
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Rodrigo Sandoval-Almazán
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to David Valle-Cruz .

Editor information

Editors and Affiliations

  1. School of Built Environment, Engineering, and Computing, Leeds Beckett University, Leeds, UK

    Dr. Muthu Ramachandran

  2. Debesis Education, Derby, UK

    Prof. Zaigham Mahmood

Rights and permissions

Reprints and Permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this chapter

Verify currency and authenticity via CrossMark

Cite this chapter

López-Chau, A., Valle-Cruz, D., Sandoval-Almazán, R. (2020). Sentiment Analysis of Twitter Data Through Machine Learning Techniques. In: Ramachandran, M., Mahmood, Z. (eds) Software Engineering in the Era of Cloud Computing. Computer Communications and Networks. Springer, Cham. https://doi.org/10.1007/978-3-030-33624-0_8

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-33624-0_8

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-33623-3

  • Online ISBN: 978-3-030-33624-0

  • eBook Packages: Computer ScienceComputer Science (R0)