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Emotional sentiment analysis of social media content for mental health safety

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Abstract

Text sentiment analysis is mostly used for the assessment of the author’s mood depending on the context. The purpose of sentiment analysis is to discover the exactness of the underlying emotion in a given situation. It has been applied to various fields, including stock market predictions, social media data on product evaluations, psychology, the judiciary, forecasting, illness prediction, agriculture, and more. Many researchers have worked on these topics and generated important insights. These outcomes are useful in the field because they (outcomes) help people comprehend the general summary quickly. Additionally, sentiment analysis aids in limiting the harmful effects of some posts on various social media sites such as Facebook and Twitter. For these reasons and more, we are proposing an approach to filter the social media content that could be emotionally harmful to the user, through getting the social networks content; for that, we have used Twitter API to get the user posts, and then, we have used API natural understanding language API tool to extract and classify the emotions of the Twitter content into five basic emotional categories—Joy, sadness, anger, fear, disgust—into an array of emotions; after that, we have defined a perfect emotion array from over 450 words from the English language. The main purpose of this comprehensive research article is to examine the proposed solution that we have conducted to improve the quality of content displayed to users emotionally.

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References

  • Abualigah LMQ (2019) Feature selection and enhanced Krill Herd algorithm for text document clustering. Springer, Berlin

    Book  Google Scholar 

  • Abualigah L, Gandomi AH, Elaziz MA, Hamad HA, Omari M, Alshinwan M, Khasawneh AM (2021) Advances in meta-heuristic optimization algorithms in big data text clustering. Electronics 10:101

    Article  Google Scholar 

  • Abualigah L, Diabat A, Mirjalili S, Abd Elaziz M, Gandomi AH (2021) The arithmetic optimization algorithm. Comput Methods Appl Mech Eng 376:113609

    Article  MathSciNet  Google Scholar 

  • Abualigah L, Yousri D, Abd Elaziz M, Ewees AA, Al-qaness M, Gandomi AH (2021) Aquila optimizer: a novel meta-heuristic optimization algorithm. Comput Ind Eng 157:107250

    Article  Google Scholar 

  • Ahmad S, Asghar MZ, Alotaibi FM, Awan I (2019) Detection and classification of social media-based extremist affiliations using sentiment analysis techniques. Hum Centric Comput Inf Sci 9:24

    Article  Google Scholar 

  • Ahmed CM, Abdelhamid D (2022) Sentiment analysis of Algerian dialect using machine learning and deep learning with Word2vec. Informatica 46:67–78. https://doi.org/10.31449/inf.v46i6.3340

    Article  Google Scholar 

  • Al Shehhi A, Thomas J, Welsch R, Grey I, Aung Z (2019) Arabia Felix 2.0: a cross-linguistic Twitter analysis of happiness patterns in the United Arab Emirates. J Big Data 6:33

    Article  Google Scholar 

  • Aloufi S, El Saddik A (2018) Sentiment identification in football-specific tweets. IEEE Access 6:78609–78621

    Article  Google Scholar 

  • Ansari L, Ji S, Chen Q, Cambria E (2022) Ensemble hybrid learning methods for automated depression detection. IEEE Trans Comput Soc Syst

  • Arias M, Arratia A, Xuriguera R (2013) Forecasting with Twitter data. ACM Trans Intell Syst Technol 5:8

    Article  Google Scholar 

  • Boivin M, Hymel S, Bukowski WM (1995) The roles of social withdrawal, peer rejection, and victimization by peers in predicting loneliness and depressed mood in childhood. Dev Psychopathol 7(04):765–785

    Article  Google Scholar 

  • Budiharto W, Meiliana M (2018) Prediction and analysis of Indonesia Presidential election from Twitter using sentiment analysis. J Big Data 5:51

    Article  Google Scholar 

  • Cambria E, Hussain A, Durrani T, Havasi C, Eckl C, Munro J (2010) Sentic computing for patient centered applications. In: IEEE 10th international conference on signal processing proceedings. IEEE, pp 1279–1282

  • Chen Q, Chaturvedi I, Ji S, Cambria E (2021) Sequential fusion of facial appearance and dynamics for depression recognition. Pattern Recogn Lett 150:115–121

    Article  Google Scholar 

  • Chou KL, Liang K, Sareen J (2011) The association between social isolation and DSMIV mood, anxiety, and substance use disorders: wave 2 of the national epidemiologic survey on alcohol and related conditions. J Clin Psychiatry 72(11):1468–1476

    Article  Google Scholar 

  • Cury RM (2019) Oscillation of tweet sentiments in the election of João Doria Jr. for Mayor. J Big Data 6:42

    Article  Google Scholar 

  • Davila J, Hershenberg R, Feinstein BA, Gorman K, Bhatia V, Starr LR (2012) Frequency and quality of social networking among young adults: associations with depressive symptoms, rumination, and co-rumination. Psychol Pop Media Cult 1(2):72–86

    Article  Google Scholar 

  • Deters F, Mehl MR (2012) Does posting Facebook status updates increase or decrease loneliness? An online social networking experiment. Soc Psychol Personal Sci 1948550612469233

  • Dick J (2013) Why do social networks increase stress? The Huffington post 11/09/2013. Retrieved 9 October 2016 from http://www.huffingtonpost.in/entry/social-networks-and-stress_b_3534170

  • Drouin M, Kaiser DH, Miller DA (2012) Phantom vibrations among undergraduates: prevalence and associated psychological characteristics. Comput Hum Behav 28:1490–1496

    Article  Google Scholar 

  • Han S, Mao R, Cambria E (2022) Hierarchical attention network for explainable depression detection on Twitter aided by metaphor concept mappings. arXiv preprint arXiv:2209.07494

  • https://cloud.ibm.com/apidocs/naturallanguage-understanding

  • https://github.com/benrouba/Emotional-Sentiment-Analysis-of-Social-Media-content-for-Mental-Health-Safety

  • Jansen BJ, Zhang M, Sobel K, Chowdury A (2009) Twitter power: Tweets as electronic word of mouth. J Am Soc Inf Sci Technol 60:2169–2188

    Article  Google Scholar 

  • Ji S, Pan S, Li X, Cambria E, Long G, Huang Z (2020) Suicidal ideation detection: a review of machine learning methods and applications. IEEE Trans Comput Soc Syst. https://doi.org/10.1109/TCSS.2020.3021467

    Article  Google Scholar 

  • Ji S, Li X, Huang Z, Cambria E (2022) Suicidal ideation and mental disorder detection with attentive relation networks. Neural Comput Appl 34(13):10309–10319

    Article  Google Scholar 

  • Komal M, Joshi MS (2016) Effectiveness of different similarity measures for text classification and clustering. Int J Comput Sci Inf Technol 7:1715–1720

    Google Scholar 

  • Leung CWK (2009) Sentiment analysis of product reviews. In: Encyclopedia of data warehousing and mining, 2nd edn. IGI Global, pp 1794–1799

  • Morente-Molinera JA, Kou G, Samuylov K, Urena R, Herrera-Viedma E (2019) Carrying out consensual Group Decision Making processes under social networks using sentiment analysis over comparative expressions. Knowl Based Syst 165:335–345

    Article  Google Scholar 

  • Mountaga D, Chayma F, Hatem H (2021) Bambara language dataset for sentiment analysis, conference paper ICLR

  • Pong-inwong C, Songpan W (2018) Sentiment analysis in teaching evaluations using sentiment phrase pattern matching (SPPM) based on association mining. Int J Mach Learn Cybern 10:2177–2186

    Article  Google Scholar 

  • Prabhu BA, Ashwini B, Khan TA, Das A (2019) Predicting election result with sentimental analysis using Twitter data for candidate selection. In: Innovations in computer science and engineering. Springer, Berlin, pp 49–55

  • Ringsquandl M, Petkovic D (2013) Analyzing political sentiment on Twitter. In: Proceedings of the 2013 AAAI spring symposium series, Stanford, pp 25–27

  • Risch J, Krestel R (2018) Aggression identification using deep learning and data augmentation. In: Proceedings of the first workshop on trolling, aggression and cyberbullying (TRAC-2018), Santa Fe, NM, USA, pp 150–158

  • Shaha A-O, Amal A-R (2022) A review and comparative analysis of sentiment analysis techniques. Informatica 46:33–44. https://doi.org/10.31449/inf.v46i6.3991

    Article  Google Scholar 

  • Souri A, Hosseinpour S, Rahmani AM (2018) Personality classification based on profiles of social network users and the five-factor model of personality. Hum Centric Comput Inf Sci 8:24

    Article  Google Scholar 

  • Subramani S, Wang H, Vu HQ, Li G (2018) Domestic violence crisis identification from Facebook posts based on deep learning. IEEE Access 6:54075–54085

    Article  Google Scholar 

  • Subramani S, Michalska S, Wang H, Du J, Zhang Y, Shakeel H (2019) Deep learning for multi-class identification from domestic violence online posts. IEEE Access 7:46210–46224

    Article  Google Scholar 

  • Yuan S, Huang H, Wu L (2016) Use of word clustering to improve emotion recognition from short text. J Comput Sci Eng 10(4):103–110

    Article  Google Scholar 

  • Ji, S., Pan, S., Li, X., Cambria, E., Long, G., & Huang, Z. (2020). Suicidal ideation detection: A review of machine learning methods and applications. IEEE Transactions on Computational Social Systems, 8(1), 214- 226.

  • Han, S., Mao, R., & Cambria, E. (2022). Hierarchical attention network for explainable depression detection on Twitter aided by metaphor concept mappings. arXiv preprint arXiv:2209.07494.

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Authors and Affiliations

  1. Computer Science Department, Badji Mokhtar University, BO 12, 23000, Annaba, Annaba, Algeria

    Ferdaous Benrouba & Rachid Boudour

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  1. Ferdaous Benrouba
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  2. Rachid Boudour
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Correspondence to Ferdaous Benrouba.

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Benrouba, F., Boudour, R. Emotional sentiment analysis of social media content for mental health safety. Soc. Netw. Anal. Min. 13, 17 (2023). https://doi.org/10.1007/s13278-022-01000-9

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