Skip to main content
SpringerLink
Log in

A Framework for Detecting User’s Psychological Tendencies on Twitter Based on Tweets Sentiment Analysis

  • Conference paper
  • First Online:
Trends in Artificial Intelligence Theory and Applications. Artificial Intelligence Practices (IEA/AIE 2020)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12144))

Abstract

The more societies develop, people have less time for interacting face-to-face with each other. Therefore, more and more users express their opinions on many topics on Twitter. The sentiments contained in these opinions are becoming a valuable source of data for politicians, researchers, producers, and celebrities. Many studies have used this data source to solve a variety of practical problems. However, most of the previous studies only focused on using the sentiment in tweets to address the issues regarding commercial without considering the negative aspects related to user psychology, such as psychological disorders, cyberbullying, antisocial behaviors, depression, and negative thoughts. These problems have a significant effect on users and societies. This paper proposes a method to detect the psychological tendency that hides insides one person and to give the causations that lead to this psychological tendency based on analyzing sentiment of tweets by combining the feature ensemble model and the convolutional neural network model. The results prove the efficacy of the proposed approach in terms of the \(F_1\) score and received information.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    http://www.internetlivestats.com/twitter-statistics/.

  2. 2.

    https://zephoria.com/twitter-statistics-top-ten/.

  3. 3.

    https://www.coindesk.com/price/bitcoin.

  4. 4.

    (https://archive.org/stream/intensifiersincu00benz/intensifiersincu00benz_djvu.txt).

  5. 5.

    http://nlp.stanford.edu/projects/glove/.

  6. 6.

    https://pypi.org/project/tweepy/.

  7. 7.

    https://pypi.org/project/emoji/.

  8. 8.

    https://pypi.org/project/aspell-python-py2/.

References

  1. Asghar, M.Z., Khan, A., Ahmad, S., Qasim, M., Khan, I.A.: Lexicon-enhanced sentiment analysis framework using rule-based classification scheme. PloS one 12(2), e0171649 (2017)

    Article  Google Scholar 

  2. Baccianella, S., Esuli, A., Sebastiani, F.: Sentiwordnet 3.0:an enhanced lexical resource for sentiment analysis and opinion mining. In: Lrec, vol. 10, pp. 2200–2204 (2010)

    Google Scholar 

  3. Collobert, R., Weston, J.: A unified architecture for natural language processing: deep neural networks with multitask learning. In: Proceedings of the 25th International Conference on Machine Learning, pp. 160–167. ACM (2008)

    Google Scholar 

  4. Phan, H.T., Tran, V.C., Nguyen, N.T., Hwang, D.: Improving the performance of sentiment analysis of tweets containing fuzzy sentiment using the feature ensemble model. IEEE Access 8, 14630–14641 (2020). https://doi.org/10.1109/ACCESS.2019.2963702

    Article  Google Scholar 

  5. Jain, A., Tripathi, S., DharDwivedi, H., Saxena, P.: Forecasting price of cryptocurrencies using tweets sentiment analysis. In: proceedings of the 2018 Eleventh International Conference on Contemporary Computing (IC3), pp. 1–7. IEEE (2018)

    Google Scholar 

  6. Kennedy, A., Inkpen, D.: Sentiment classification of movie reviews using contextual valence shifters. Comput. Intell. 22(2), 110–125 (2006)

    Article  MathSciNet  Google Scholar 

  7. Kim, Y.: Convolutional neural networks for sentence classification (2014). arXiv preprint arXiv:1408.5882

  8. Kiritchenko, S., Mohammad, S.M.: The effect of negators, modals, and degree adverbs on sentiment composition (2017). arXiv preprint arXiv:1712.01794

  9. Loper, E., Bird, S.: NLTK: the natural language toolkit (2002). arXiv preprint cs/0205028

    Google Scholar 

  10. McManus, K., Mallory, E.K., Goldfeder, R.L., Haynes, W.A., Tatum, J.D.: Mining Twitter data to improve detection of schizophrenia. AMIA Summits Transl. Sci. Proc. 2015, 122 (2015)

    Google Scholar 

  11. Nabil, S., Elbouhdidi, J., Yassin, M.: Recommendation system based on data analysis-application on tweets sentiment analysis. In: Proceedings of the 2018 IEEE 5th International Congress on Information Science and Technology (CiSt), pp. 155–160. IEEE (2018)

    Google Scholar 

  12. O’Dea, B., Wan, S., Batterham, P.J., Calear, A.L., Paris, C., Christensen, H.: Detecting suicidality on Twitter. Internet Interventions 2(2), 183–188 (2015)

    Article  Google Scholar 

  13. Phan, H.T., Nguyen, N.T., Hwang, D.: A tweet summarization method based on maximal association rules. In: Nguyen, N.T., Pimenidis, E., Khan, Z., Trawiński, B. (eds.) ICCCI 2018. LNCS (LNAI), vol. 11055, pp. 373–382. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-98443-8_34

    Chapter  Google Scholar 

  14. Phan, H.T., Tran, V.C., Nguyen, N.T., Hwang, D.: Decision-making support method based on sentiment analysis of objects and binary decision tree mining. In: Wotawa, F., Friedrich, G., Pill, I., Koitz-Hristov, R., Ali, M. (eds.) IEA/AIE 2019. LNCS (LNAI), vol. 11606, pp. 753–767. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-22999-3_64

    Chapter  Google Scholar 

  15. Subramaniyaswamy, V., Logesh, R., Abejith, M., Umasankar, S., Umamakeswari, A.: Sentiment analysis of tweets for estimating criticality and security of events. J. Organ. End User Comput. (JOEUC) 29(4), 51–71 (2017)

    Article  Google Scholar 

  16. Taboada, M., Brooke, J., Tofiloski, M., Voll, K., Stede, M.: Lexicon-based methods for sentiment analysis. Comput. Linguist. 37(2), 267–307 (2011)

    Article  Google Scholar 

  17. Tomihira, T., Otsuka, A., Yamashita, A., Satoh, T.: What does your tweet emotion mean?: neural emoji prediction for sentiment analysis. In: Proceedings of the 20th International Conference on Information Integration and Web-based Applications & Services, pp. 289–296. ACM (2018)

    Google Scholar 

  18. Trang Phan, H., Nguyen, N.T., Tran, V.C., Hwang, D.: A sentiment analysis method of objects by integrating sentiments from tweets. J. Intell. Fuzzy Syst. (Preprint) 37(6), 1–13 (2019)

    Google Scholar 

  19. Yang, W., Lan, M., Shen, Y.: Effect of climate and seasonality on depressed mood among twitter users. Appl. Geogr. 63, 184–191 (2015)

    Article  Google Scholar 

  20. Yussupova, N., Boyko, M., Bogdanova, D., Hilbert, A.: A decision support approach based on sentiment analysis combined with data mining for customer satisfaction research. Int. J. Adv. Intell. Syst. 8(1&2), 145–158 (2015). Published by IARIA

    Google Scholar 

  21. Dinakar, S., Andhale, P., Rege, M.: Sentiment analysis of social network content. In: Proceedings of the 2015 IEEE International Conference on Information Reuse and Integration, San Francisco, CA, pp. 189–192 (2015). https://doi.org/10.1109/IRI.2015.37

Download references

Acknowledgment

This research was supported by the National Research Foundation of Korea (NRF) grant funded by the BK21PLUS Program (22A20130012009).

Author information

Authors and Affiliations

  1. Department of Computer Engineering, Yeungnam University, Gyeongsan, Republic of Korea

    Huyen Trang Phan & Dosam Hwang

  2. Faculty of Engineering and Information Technology, Quang Binh University, Dong Hoi, Vietnam

    Van Cuong Tran

  3. Faculty of Computer Science and Management, Wroclaw University of Science and Technology, Wroclaw, Poland

    Ngoc Thanh Nguyen

Authors
  1. Huyen Trang Phan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Van Cuong Tran
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ngoc Thanh Nguyen
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Dosam Hwang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Dosam Hwang .

Editor information

Editors and Affiliations

  1. Iwate Prefectural University, Takizawa, Japan

    Hamido Fujita

  2. Harbin Institute of Technology (Shenzhen), Shenzhen, China

    Philippe Fournier-Viger

  3. Texas State University, San Marcos, TX, USA

    Moonis Ali

  4. Iwate Prefectural University, Takizawa, Japan

    Jun Sasaki

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Phan, H.T., Tran, V.C., Nguyen, N.T., Hwang, D. (2020). A Framework for Detecting User’s Psychological Tendencies on Twitter Based on Tweets Sentiment Analysis. In: Fujita, H., Fournier-Viger, P., Ali, M., Sasaki, J. (eds) Trends in Artificial Intelligence Theory and Applications. Artificial Intelligence Practices. IEA/AIE 2020. Lecture Notes in Computer Science(), vol 12144. Springer, Cham. https://doi.org/10.1007/978-3-030-55789-8_32

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-55789-8_32

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-55788-1

  • Online ISBN: 978-3-030-55789-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation