The Anxieties Detection from Tweets about Distance Learning with Negative Sentiments

Yulia Dyulicheva


The study of the mental health of society and the automatic detection of various types of disorders based on text analysis is one of the priority areas of e-health. The empirical investigation of the various anxieties and their causes in the pandemic period connecting with distance learning introduction based on an analysis of 71475 tweets is performed in the paper. The analysis of hashtags of tweets with negative sentiment and proposed patterns based on part of speech recognition allows us to extract regularities that describe sensations and causes of emotions associated with the appearance of the affective state. We created a dictionary of terms describing affective states based on the most frequent words with negative sentiment. Particular attention was paid to feature engineering using positive words from positive tweets and negative words from negative tweets, taking into account parts of speech. The proposed approach to feature engineering made it possible to reduce the dimension of the feature space while maintaining the quality of the classification (84%).   

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Z. Kastrati, F. Dalipi, A.S. Imran, K.P. Nuci and M.A. Wani, “Sentiment Analysis of Students’ Feedback with NLP and Deep Learning: A Systematic Mapping Study,” in Applied Sciences, vol. 11 (3986), 1, pp. 1– 23, Apr. 2021.

K. Gimpel, N. Schneider, B. O’Connor, D. Das, D. Mills, J. Eisenstein, M. Heilman, D. Yogatama, J. Flanigan and N.A. Smith, “Part-of-speech tagging for twitter: Annotation, features, and experiments,” in Proc of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: short papers (Association for Computational Linguistics. Portland, Oregon, USA, vol.2, pp.42-47, 2011.

X. Zhou, X. Tao, J. Yong and Z. Yang, “Sentiment Analysis on Tweets for Social Events,” in Proc of the 2013 IEEE 17th International Conference on Computer Supported Cooperative Work in Design (IEEE, Whistler, BC, Canada,), vol. 13710533, pp. 557– 562, 2013.

V. Raghupathi, J. Ren and W. Raghupathi, “Studying Public Perception about Vaccination: A Sentiment Analysis of Tweets,” in Int J Environ Res Public Health, vol. 17(10): 3464, May. 2020.

J.C. Lyu, E.L. Han, G.K. and Luli, “COVID-19 Vaccine-Related Discussion on Twitter: Topic Modeling and Sentiment Analysis,” in J Med Internet Res, vol. 23(6). 2021.

E. Sasmaz and F.B. Tek, “Tweet Sentiment Analysis for Cryptocurrencies,” in Int Conference on Computer Science and Engineering (IEEE, Ankara, Turkey), vol. 2. 2021.

T.Wijesiriwardene,H.Inan,U.Kursuncu1,M.Gaur, V.L. Shalin, K. Thirunarayan, A. Sheth and I. Budak, “ALONE: A Dataset for Toxic Behavior among Adolscents on Twitter,” in arXiv:2008.06465, pp. 1–13. 2020.

A. M. Khattak, R. Batool, F. A. Satti, J. Hussain, W. A. Khan, A. M. Khan and B. Hayat, “Tweets classification and sentiment analysis for personalized tweets recommendation,” in Complexity. 2020.

V.A. Kharde and S. Sonawane, “Sentiment Analysis of Twitter Data: A Survey of Techniques,” in Int Journal of Computer Applications, vol. 139, 5, pp.5-15. 2016.

R.A. Oyekunle and R.O. Abdulkareem, “Sentiment Analysis of Students’ Tweets on Unilorin CBT Examinations,” in Int Journal of Applied Business and Information Systems, vol. 3(2), pp. 66–75. 2019.

H. Al-Rubaiee and K. Alomar, “Clustering Students’ Arabic Tweets using Different Schemes,” in Int Journal of Advanced Computer Science and Applications, vol. 8(276). 2017.

E.F. Felipe Bravo-Marquez and B. Pfahringer, “From Unlabelled Tweets to Twitter-specific Opinion Words,” in Proc of 38th Int ACM SIGIR Conference on Research and Development (New York, USA: ACM, Santiago de Chile), pp. 743–746. 2015.

X.Chen,M.Vorvoreanu and K.Madhavan, “Mining Social Media Data for Understanding Students’ Learning Experiences,” in IEEETrans on Learning Technologies, vol. 7(246). 2014.

M. Mujahid, E. Lee, F. Rustam, P.B. Washington, S. Ullah, A.A. Reshi and I. Ashraf, “Sentiment Analysis and Topic Modeling on Tweets about Online Education during COVID-19,” in Applied Sciences, vol. 11(1). 2021.

M. Aljabri, S.M.B. Chrouf, N.A. Alzahrani, L. Al- ghamdi, R. Alfehaid, R. Alqarawi, J. Alhuthayfi and N. Alduhailan, “Sentiment Analysis of Arabic Tweets Regarding Distance Learning in Saudi Arabia during the COVID-19 Pandemic,” in Sensors, vol. 21(1). 2021.

N. Arambepola, “Analysing the Tweets about Distance Learning during COVID-19 Pandemic using Sentiment Analysis,” in Proc of the International Conference on Advances in Computing and Technology , pp. 169–171. 2020.

M. Yoshida, “Investigation of University Students’ Behaviour in a Heterarchical Twitter Community,” in Education and Information Technologies, vol. 26(1). 2021.

Y. Drias and H. Drias, “Sentiment Evolution Analysis and Association Rule Mining for COVID-19 Tweets,” in Journal of Digital Art and Humanities. pp. 3–21. 2021.

P.Chikersal, S.Poria and,E.Cambria, “SeNTU:Sentiment Analysis of Tweets by Combining a Rule-based Classifier with Supervised Learning,” in Proc of the 9th International Workshop on Semantic Evaluaion Association for Computational Linguistics, Denver, Colorado. pp. 647–651. 2015.

M. Pota, M. Ventura, R. Catelli and M. Esposito, “An Effective BERT-Based Pipeline for Twitter Sentiment Analysis: A Case Study in Italian,” in Sensors, vol. 21(1). 2021.

W. Wolny, “Emotion Analysis of Twitter Data that use Emoticons and Emoji Ideograms,” in 25th Int Conference on Information Systems Development. pp. 1–10. 2016.

E.A.H. Khalil, E.M.E. Houby and H.K. Mohamed, “Deep Learning for Emotion Analysis in arabic Tweets,” in J Big Data, vol. 8(136). 2021.

S. Tam, R. B. Said and Ö. Ö. Tanriöver, “A ConvBiLSTM Deep Learning Model-Based Approach for Twitter Sentiment Classification,” in IEEE Access, vol. 9. pp. 41283-41293. 2021.

S. Das and A.K. Kolya, “Predicting the pandemic: sentiment evaluation and predictive analysis from large-scale tweets on Covid-19 by deep convolutional neural network,” in Evol Intell. pp. 1– 22. Mar 2021.

C. Sitaula, A. Basnet, A. Mainali and T.B. Shahi, “Deep Learning-Based Methods for Sentiment Analysis on Nepali COVID-19-Related Tweets,” in Computational Intelligence and Neuroscience, vol.2021(2158184). 2021.

Healey, Ramaswamy. (2022, Apr 10) Tweet Sentiment Visualization [Online]. Available:

B. Hasdemir. (2022, Apr 10) Tweet about Distance Learning [Online]. Available: tweets-about-distance-learning

B. Hasdemir. (2022, Apr 10) Sentiment analysis on the tweets about distance learning with TextBlob [Online]. Available: sentiment-analysis-on-the-tweets-about-distance-learning-with-textblob-/cc73702b48bc

C.J. Hutto, E. Gilbert, “VADER: A Parsimonious Rule-based Model for Sentiment Analysis of Social Media Text, “ in Proceedings of the Eighth International AAAI Conference on Weblogs and Social Media. pp. 216-225. 2014.

S. Ghosh, M.S. Desarkar, “Class Specific TF-IDF Boosting for Short-text Classification,” in WWW '18: Companion Proceedings of the The Web Conference, pp. 1629-1637. 2018.

F. Valencia, A. Gomez-Espinosa and B. Valdes-Aguirre, “Price Movement Prediction of Cryptocurrencies using Sentiment Analysis and Machine Learning,” in Entropy, vol.21(589). 2019.


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