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Perceptible sentiment analysis of students' WhatsApp group chats in valence, arousal, and dominance space

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Abstract

Sentiment analysis is a vastly established domain for social media monitoring, feedback insights, and commercial or political campaigns. It allows us to gain an overview of the wider public opinion on certain topics. Nowadays, different social media platforms play a crucial role in web-based sentiment analysis and emotion detection from distinct perspectives. Likewise, WhatsApp is probably the most popular messaging app, allowing users to send messages, images, audio, or videos. However, it is still highly under-explored for any type of linguistic synthesis and analysis. Like many other groups of people, students use WhatsApp for various purposes, even more since the last two years of the pandemic phase, for instance, class communication, study group communication, etc. In this paper, we present a novel approach to analyze the sentiments and emotions of students in valence, arousal, and dominance space by classifying the messages from their WhatsApp group chat. The emotional dimensions of valence, arousal and dominance (VAD) can derive a person’s interest (attraction), level of activation, and perceived level of control for a particular situation from textual communication. We propose a vanilla SVM model fused with a language classifier to calculate each message's sentiment ratings. Finally, using the SVM classifier, we classify the sentiment ratings concerning the degree of the VAD scale. The data were analyzed using a qualitative content analysis method. The results of the study in the form of cumulative sentiment scale and sentiment clustering in VAD space reveal that the students' WhatsApp groups were mostly used for sharing information, exchanging ideas, and discussing issues, with mostly neutral to positive sentiment viewpoints for the provided topics of discussions.

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Notes

  1. https://www.businessofapps.com/data/whatsapp-statistics/.

References

  • Araújo M, Pereira A, Benevenuto F (2020) A comparative study of machine translation for multilingual sentence-level sentiment analysis. Inf Sci 512:1078–1102

    Article  Google Scholar 

  • Araujo M, Reis J, Pereira A, Benevenuto F (2016) An evaluation of machine translation for multilingual sentence-level sentiment analysis. In: Proceedings of the 31st annual ACM symposium on applied computing, pp 1140–1145

  • Behdenna S, Barigou F, Belalem G (2018) Document level sentiment analysis: a survey. EAI Endorsed Trans Context-Aware Syst Appl 4(13):e2–e2

    Google Scholar 

  • Bhatia P, Ji Y, Eisenstein J (2015) Better document-level sentiment analysis from RST discourse parsing. In: Proceedings of the 2015 conference on empirical methods in natural language processing, pp 2212–2218

  • Buechel S, Hahn U (2017). EmoBank: studying the impact of annotation perspective and representation format on dimensional emotion analysis. In: Proceedings of the 15th conference of the European chapter of the Association for Computational Linguistics: volume 2, short papers, pp 578–585

  • Calvo RA, Mac Kim S (2013) Emotions in text: dimensional and categorical models. Comput Intell 29(3):527–543

    Article  MathSciNet  Google Scholar 

  • Canales L, Martínez-Barco P (2014) Emotion detection from text: a survey. In: Proceedings of the workshop on natural language processing in the 5th information systems research working days (JISIC), pp 37–43

  • Chatterji S, Varshney N, Rahul RK (2017) AspectFrameNet: a frameNet extension for analysis of sentiments around product aspects. J Supercomput 73(3):961–972

    Article  Google Scholar 

  • Chen G, Tian Y, Song Y (2020) Joint aspect extraction and sentiment analysis with directional graph convolutional networks. In: Proceedings of the 28th international conference on computational linguistics, pp 272–279

  • Dabiri S, Heaslip K (2019) Developing a Twitter-based traffic event detection model using deep learning architectures. Expert Syst Appl 118:425–439

    Article  Google Scholar 

  • Dang E, Hu Z, Li T (2022) Enhancing collaborative filtering recommender with prompt-based sentiment analysis. arXiv:2207.12883

  • Das S, Kolya AK (2017) Sense GST: text mining & sentiment analysis of GST tweets by Naive Bayes algorithm. In: 2017 third international conference on research in computational intelligence and communication networks (ICRCICN). IEEE, pp 239–244

  • Das S, Das D, Kolya AK (2020a) An approach for sentiment analysis of GST tweets using words popularity versus polarity generation. In: Computational intelligence in pattern recognition. Springer, Singapore, pp 69–80

  • Das S, Das D, Kolya AK (2020b) Sentiment classification with GST tweet data on LSTM based on polarity-popularity model. Sādhanā 45(1):1–17

    Article  Google Scholar 

  • Devlin J, Chang MW, Lee K, Toutanova K (2019) BERT: pre-training of deep bidirectional transformers for language understanding. In Proceedings of the 2019 conference of the North American chapter of the Association for Computational Linguistics: human language technologies, volume 1 (long and short papers), pp 4171–4186

  • Dey A, Jenamani M, Thakkar JJ (2018) Senti-N-Gram: an n-gram lexicon for sentiment analysis. Expert Syst Appl 103:92–105

    Article  Google Scholar 

  • Dragoni M, Poria S, Cambria E (2018) OntoSenticNet: a commonsense ontology for sentiment analysis. IEEE Intell Syst 33(3):77–85

    Article  Google Scholar 

  • Gao T, Fang J, Liu H, Liu Z, Liu C, Liu P, Bao Y, Yan W (2022) LEGO-ABSA: a prompt-based task assemblable unified generative framework for multi-task aspect-based sentiment analysis. In Proceedings of the 29th international conference on computational linguistics, pp 7002–7012

  • Gong L, Haines B, Wang H (2017) Clustered model adaption for personalized sentiment analysis. In: Proceedings of the 26th international conference on World Wide Web, pp 937–946

  • Han J, Zhang Z, Cummins N, Schuller B (2019) Adversarial training in affective computing and sentiment analysis: recent advances and perspectives. IEEE Comput Intell Mag 14(2):68–81

    Article  Google Scholar 

  • He X, Gao J, Deng L (2014) Deep learning for natural language processing and related applications (Tutorial at ICASSP). In: IEEE international conference on acoustics, speech, and signal processing (ICASSP)

  • He K, Mao R, Gong T, Li C, Cambria E (2022) Meta-based self-training and re-weighting for aspect-based sentiment analysis. In: IEEE Transactions on Affective Computing, 2022, pp 1–13. https://doi.org/10.1109/TAFFC.2022.3202831

  • Hu M, Liu B (2004). Mining and summarizing customer reviews. In: Proceedings of the tenth ACM SIGKDD international conference on knowledge discovery and data mining, pp 168–177

  • Hu X, Tang L, Tang J, Liu H (2013) Exploiting social relations for sentiment analysis in microblogging. In: Proceedings of the sixth ACM international conference on Web search and data mining, pp 537–546

  • Jiang L, Yu M, Zhou M, Liu X, Zhao T (2011) Target-dependent twitter sentiment classification. In: Proceedings of the 49th annual meeting of the association for computational linguistics: human language technologies, pp 151–160

  • Jindal S, Sharma K (2018) Intend to analyze Social Media feeds to detect behavioral trends of individuals to proactively act against Social Threats. Procedia Comput Sci 132:218–225

    Article  Google Scholar 

  • Kucher K, Paradis C, Kerren A (2018) The state of the art in sentiment visualization. In: Computer graphics forum, vol 37, no 1, pp 71–96

  • Lee LH, Li JH, Yu LC (2022) Chinese EmoBank: building valence-arousal resources for dimensional sentiment analysis. Trans Asian Low-Resour Lang Inf Process 21(4):1–18

    Article  Google Scholar 

  • Li C, Gao F, Bu J, Xu L, Chen X, Gu Y, Shao Z, Zheng Q, Zhang N, Wang Y, Yu Z (2021) Sentiprompt: sentiment knowledge enhanced prompt-tuning for aspect-based sentiment analysis. arXiv:2109.08306

  • Liang B, Su H, Gui L, Cambria E, Xu R (2022) Aspect-based sentiment analysis via affective knowledge enhanced graph convolutional networks. Knowl-Based Syst 235:107643

    Article  Google Scholar 

  • Liu B (2012) Sentiment analysis and opinion mining. In: Synthesis lectures on human language technologies, vol 5, no 1, pp 1–167

  • Liu Y, Ott M, Goyal N, Du J, Joshi M, Chen D, Levy O, Lewis M, Zettlemoyer L, Stoyanov V (2019) Roberta: a robustly optimized bert pretraining approach. arXiv:1907.11692

  • Ma B, Yuan H, Wu Y (2017) Exploring performance of clustering methods on document sentiment analysis. J Inf Sci 43(1):54–74

    Article  Google Scholar 

  • Ma Y, Peng H, Khan T, Cambria E, Hussain A (2018a) Sentic LSTM: a hybrid network for targeted aspect-based sentiment analysis. Cogn Comput 10(4):639–650

    Article  Google Scholar 

  • Ma Y, Peng H, Cambria E (2018b) Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI conference on artificial intelligence, vol 32, no 1

  • Mohammad S, Bravo-Marquez F, Salameh M, Kiritchenko S (2018) Semeval-2018 task 1: affect in tweets. In: Proceedings of the 12th international workshop on semantic evaluation, pp 1–17

  • Montoyo A, MartíNez-Barco P, Balahur A (2012) Subjectivity and sentiment analysis: an overview of the current state of the area and envisaged developments. Decis Support Syst 53(4):675–679

    Article  Google Scholar 

  • Morente-Molinera JA, Kou G, Peng Y, Torres-Albero C, Herrera-Viedma E (2018) Analysing discussions in social networks using group decision making methods and sentiment analysis. Inf Sci 447:157–168

    Article  Google Scholar 

  • Mubarok MS, Adiwijaya, Aldhi MD (2017) Aspect-based sentiment analysis to review products using Naïve Bayes. In: AIP conference proceedings, vol 1867, no 1. AIP Publishing LLC, p 020060

  • Nath G, Adhi G (2019) An attempt to detect fake messages circulated on WhatsApp. In: Proceedings of 7th international conference of business analytics and intelligence

  • Pak A, Paroubek P (2010) Twitter as a corpus for sentiment analysis and opinion mining. In: LREc, vol 10, no 2010, pp 1320–1326

  • Pandarachalil R, Sendhilkumar S, Mahalakshmi GS (2015) Twitter sentiment analysis for large-scale data: an unsupervised approach. Cogn Comput 7(2):254–262

    Article  Google Scholar 

  • Peng H, Cambria E, Zou X (2017) Radical-based hierarchical embeddings for Chinese sentiment analysis at sentence level. In: The thirtieth international flairs conference

  • Provoost S, Ruwaard J, van Breda W, Riper H, Bosse T (2019) Validating automated sentiment analysis of online cognitive behavioral therapy patient texts: an exploratory study. Front Psychol 10:1065

    Article  Google Scholar 

  • Resende G, Melo P, CS Reis J, Vasconcelos M, Almeida JM, Benevenuto F (2019) Analyzing textual (mis) information shared in WhatsApp groups. In: Proceedings of the 10th ACM conference on web science, pp 225–234

  • Ribeiro MT, Wu T, Guestrin C, Singh S (2020) Beyond accuracy: behavioral testing of NLP models with CheckList. arXiv:2005.04118

  • Rintyarna BS, Sarno R, Fatichah C (2019) Evaluating the performance of sentence level features and domain sensitive features of product reviews on supervised sentiment analysis tasks. J Big Data 6(1):1–19

    Article  Google Scholar 

  • Schubert M, Durruty D, Joyner DA (2018) Measuring learner tone and sentiment at scale via text analysis of forum posts. In: Proceedings of the 8th edition of the international workshop on personalization approaches in learning environments (PALE). London, United Kingdom

  • Tang D, Wei F, Qin B, Yang N, Liu T, Zhou M (2015) Sentiment embeddings with applications to sentiment analysis. IEEE Trans Knowl Data Eng 28(2):496–509

    Article  Google Scholar 

  • Thelwall M, Buckley K, Paltoglou G, Cai D, Kappas A (2010) Sentiment strength detection in short informal text. J Am Soc Inform Sci Technol 61(12):2544–2558

    Article  Google Scholar 

  • Thelwall M, Buckley K, Paltoglou G, Skowron M, Garcia D, Gobron S, Ahn J, Kappas A, Küster D, Holyst JA (2013) Damping sentiment analysis in online communication: discussions, monologs and dialogs. In: International conference on intelligent text processing and computational linguistics. Springer, Berlin, pp 1–12

  • Tian Y, Chen G, Song Y (2021) Aspect-based sentiment analysis with type-aware graph convolutional networks and layer ensemble. In: Proceedings of the 2021 conference of the North American chapter of the Association for Computational Linguistics: human language technologies, pp 2910–2922

  • Turney P (2002) Thumbs up or thumbs down? Semantic orientation applied to unsupervised classification of reviews. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 417–424

  • Wang L, Niu J, Yu S (2019a) SentiDiff: combining textual information and sentiment diffusion patterns for Twitter sentiment analysis. IEEE Trans Knowl Data Eng 32(10):2026–2039

    Article  Google Scholar 

  • Wang J, Yu LC, Lai KR, Zhang X (2019b) Tree-structured regional CNN-LSTM model for dimensional sentiment analysis. IEEE/ACM Trans Audio Speech Lang Process 28:581–591

    Article  Google Scholar 

  • Wang S, Pan L, Wu Y (2022) Meta-information fusion of hierarchical semantics dependency and graph structure for structured text classification. ACM Trans Knowl Discov Data (TKDD). https://doi.org/10.1145/3537971

  • Wiebe J, Bruce R, O’Hara TP (1999) Development and use of a gold-standard data set for subjectivity classifications. In: Proceedings of the 37th annual meeting of the Association for Computational Linguistics, pp 246–253

  • Xia R, Xu F, Yu J, Qi Y, Cambria E (2016) Polarity shift detection, elimination and ensemble: a three-stage model for document-level sentiment analysis. Inf Process Manag 52(1):36–45

    Article  Google Scholar 

  • Xie H, Lin W, Lin S, Wang J, Yu LC (2021) A multi-dimensional relation model for dimensional sentiment analysis. Inf Sci 579:832–844

    Article  MathSciNet  Google Scholar 

  • Yu LC, Wang J, Lai KR, Zhang X (2017) Refining word embeddings using intensity scores for sentiment analysis. IEEE/ACM Trans Audio Speech Lang Process 26(3):671–681

    Article  Google Scholar 

  • Zheng W, Yan L, Wang FY, Gou C (2021) Learning from the negativity: deep negative correlation meta-learning for adversarial image classification. In: International conference on multimedia modeling. Springer, Cham, pp 531–540

  • Zhou P, Qi Z, Zheng S, Xu J, Bao H, Xu B (2016) Text classification improved by integrating bidirectional LSTM with two-dimensional max pooling. In: Proceedings of COLING 2016, the 26th international conference on computational linguistics: technical papers, pp 3485–3495

  • Zhou D, Zhang M, Zhang L, He Y (2021) A neural group-wise sentiment analysis model with data sparsity awareness. In: Proceedings of the AAAI conference on artificial intelligence, vol 35, no 16, pp 14594–14601

  • Zhu S, Li S, Zhou G (2019). Adversarial attention modeling for multi-dimensional emotion regression. In: Proceedings of the 57th annual meeting of the association for computational linguistics, pp 471–480

  • Zucco C, Calabrese B, Cannataro M (2017) Sentiment analysis and affective computing for depression monitoring. In 2017 IEEE international conference on bioinformatics and biomedicine (BIBM). IEEE, pp 1988–1995

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  1. Department of Computer Science and Engineering (Artificial Intelligence and Machine Learning), Future Institute of Technology, Kolkata, India

    Baishali Roy & Sourav Das

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  1. Baishali Roy
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The first author had major experimental contribution in this work, while the second (corresponding) author guided the first author and helped in writing the paper.

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Correspondence to Sourav Das.

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Roy, B., Das, S. Perceptible sentiment analysis of students' WhatsApp group chats in valence, arousal, and dominance space. Soc. Netw. Anal. Min. 13, 9 (2023). https://doi.org/10.1007/s13278-022-01016-1

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