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A Method Proposed for Estimating Depressed Feeling Tendencies of Social Media Users Utilizing Their Data

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Proceedings of the 16th International Conference on Hybrid Intelligent Systems (HIS 2016) (HIS 2016)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 552))

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Abstract

The enormous progress of social networks and the large amount of data generated by them, has led many studies the possibility to identify the hidden knowledge. Depression is more than just feeling unhappy or fed for a few days and affects people in different ways and can cause a variety of symptoms. In this paper, we aim to analysis on research for feelings of depression for user’s activities in Twitter, which is a popular microblogging site, for estimating his/her depressive tendency. Then we investigate Weka as a tool of machine learning classification to extract useful information for classification of Twitter Data collected from Twitter based Twitter API. Therefore, we perform our experiments to estimate participants depressive tendencies using an algorithm for computing the semantic similarity between tweets in training set and data set based on WordNet as an external semantic resource. Experimental results show that Twitter could be used to analyze online depression feeling. In addition, this study demonstrated that we can extract sentiments of Twitter users from social networks.

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References

  1. Po-Wei Liang, B.-R.D.: Opinion mining on social media data. In: IEEE 14th International Conference on Mobile Data Management, pp. 91–96 (2013)

    Google Scholar 

  2. Statistic Brain. Twitter Statistics (2014). http://www.statisticbrain.com/twitter-statistics/

  3. Li, R., Lei, K.H., Khadiwala, Chang, R.: TEDAS: a twitter-based event detection and analysis system. In: IEEE 28th International Conference on Data Engineering, ICDE 2012, pp. 1273–1276 (2012)

    Google Scholar 

  4. Chih-Hua T., Zheng-Han T., Yung-Sheng L., Yue-Shan C.: Mental disorder detection and measurement using latent Dirichlet allocation and SentiWordNet. In: IEEE International Conference on Systems, Man, and Cybernetics, pp. 1215–1220 (2015)

    Google Scholar 

  5. https://twitter.com/Freddy_E

  6. https://twitter.com/CapitalSTEEZ_

  7. Zhao, D., Rosson, M.B.: (n.d). http://research.ihost.com/cscw08-socialnetworkinginorgs/papers/zhao_cscw08_workshop.pdf

  8. Ramirez-Esparza, N., Chung, Ewa Kacewicz, C.K., Pennebaker, J.W.: The psychology of word use in depression forums in English and in Spanish: testing two text analytic approaches. Association for the Advancement of Artificial Intelligence. www.aaai.org

  9. Vinodhini, G., Chandrasekaran, R.: Sentiment analysis and opinion mining: a survey. Int. J. 2(6) (2012)

    Google Scholar 

  10. Turney, P.: Thumbs up or thumbs down Semantic orientation applied to unsupervised classification of reviews. In: Proceedings of the Association for Computational Linguistics

    Google Scholar 

  11. Hu, M., Liu, B.: Mining and summarizing customer reviews. In: Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2004, pp. 168–177

    Google Scholar 

  12. Wilson, T., Wiebe, J., Hoffmann P.: Recognizing contextual polarity in phrase-level sentiment analysis. In: The Advanced Research and Development Activity (ARDA)

    Google Scholar 

  13. Wu, Y., Zhang, Q., Huang, X., Wu, L.: Structural opinion mining for graph-based sentiment representation. In: Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing, EMNLP-2011 (2011)

    Google Scholar 

  14. Wu, Z., Palmer, M.: Verb semantics and lexical selection. In: Proceedings of the 32nd Annual meeting of the Associations for Computational Linguistics, pp 133–138 (1994)

    Google Scholar 

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Author information

Authors and Affiliations

  1. LAROSERI Laboratory, Department of Computer Sciences, Faculty of Sciences, University of Chouaib Doukkali, El Jadida, Morocco

    Marouane Birjali & Abderrahim Beni-Hssane

  2. TIAD Laboratory, Department of Computer Sciences, Faculty of Sciences and Technologies, University of Sultan Moulay Slimane, Béni Mellal, Morocco

    Mohammed Erritali

Authors
  1. Marouane Birjali
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  2. Abderrahim Beni-Hssane
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  3. Mohammed Erritali
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Corresponding author

Correspondence to Marouane Birjali .

Editor information

Editors and Affiliations

  1. (MIR Labs), Machine Intelligence Research Labs, Auburn, Washington, USA

    Ajith Abraham

  2. Hassan 1st University, Settat, Morocco

    Abdelkrim Haqiq

  3. ENIS, University of Sfax, Sfax, Tunisia

    Adel M. Alimi

  4. Technopolis Rabat-Shore Rocade, International University of Rabat, Sala el Jadida, Morocco

    Ghita Mezzour

  5. Inst Applied Dept of Electronics, Taffala, University of Sousse, Sousse, Tunisia

    Nizar Rokbani

  6. Fakulti Teknologi Maklumat dan Komunikas, Universiti Teknikal Malaysia Melaka, Durian Tunggal, Malaysia

    Azah Kamilah Muda

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Birjali, M., Beni-Hssane, A., Erritali, M. (2017). A Method Proposed for Estimating Depressed Feeling Tendencies of Social Media Users Utilizing Their Data. In: Abraham, A., Haqiq, A., Alimi, A., Mezzour, G., Rokbani, N., Muda, A. (eds) Proceedings of the 16th International Conference on Hybrid Intelligent Systems (HIS 2016). HIS 2016. Advances in Intelligent Systems and Computing, vol 552. Springer, Cham. https://doi.org/10.1007/978-3-319-52941-7_41

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  • DOI: https://doi.org/10.1007/978-3-319-52941-7_41

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-52940-0

  • Online ISBN: 978-3-319-52941-7

  • eBook Packages: EngineeringEngineering (R0)

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