Stress Analytical Modelling Based on People’s Views on Social Networks


  • John .
  • Vivia Mary
  • Prashar .
  • Aastha .
  • Khamesra .
  • Garvit .
  • V Garvit







With the growing world, the human mind has grown too much with its own complexities. Gone are the days where people used to express themselves through speech or by verbal contact. Now, the era of social media has brought an interface to the world where they can convey their opinions as well as their inner most thoughts through various social networks. People are more comfortable to express their emotions on these social media rather in the real world. This all has led to the need of Sentiment analysis. It has a major role in detecting stress in humans and how surrounding environment is affecting the population of the world. The project analyses the stress among people through tweets. Self-report questionnaires face to face interviews wearable sensors is the main basis of psychological stress that is caused traditionally. The project covers all possible aspects of interactions on social media. Firstly, by fetching tweets from twitter dynamically based on keyword entered by user and segregating them into positive, negative and neutral categories using Naive Bayes algorithm. Secondly, performing sentiment analysis on a dataset containing movie reviews and thirdly, on a very large dataset containing 5 million tweets using Hadoop and an added algorithm of logistic regression for improved performance and efficiency. The entire project was carried out using a distinct step by step procedure consisting of data collection, data cleaning, training of data, data modelling, algorithm application and visualization. Experiments were conducted on an extensive basis to verify the superior theory algorithms and credibility of the project.



[1] Yuan Zhang, Jie Tang, Jimeng Sun, Yiran Chen, and Jinghai Rao(2016). “Moodcast: Emotion prediction via dynamic continuous factor graph model.†IEEE International Conference on Data Mining.

[2] Andrey Bogomolov, Bruno Lepri, Michela Ferron, Fabio Pianesi, and Alex Pentland (2014). “Daily stress recognition from mobile phone data, weather conditions and individual traits.†In ACM International Conference on Multimedia, 477–486.

[3] Chris Buckley and EllenM Voorhees(2004). “Retrieval evaluation with incomplete information.†In Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval, 25–32.

[4] Xiaojun Chang, Yi Yang, Alexander G Hauptmann, Eric P Xing, and Yao-Liang Yu(2015). “Semantic concept discovery for large-scale zero-shot event detection.†In Proceedings of International Joint Conference on Artificial Intelligence, 2234–2240.

[5] Wanxiang Che, Zhenghua Li, and Ting Liu(2010). “LTP: A Chinese language technology platform.†In International Conference on Computational Linguistics, 13–16.

[6] Chih chung Chang and Chih-Jen Lin(2001). “LIBSVM: a library for support vector machines.“ ACM International Conference on Intelligent Systems And Technology, 2(3):389–396.

[7] Frank R. Kschischang, Brendan J. Frey(2015). “Factor Graphs and the Sum- Product Algorithm.†IEEE Transactions.

[8] Xiao jun Chang, Yi Yang1, Alexander G. Hauptmann, Eric P. Xing and Yao- Liang Yu(2015). “Semantic Concept Discovery for Large-Scale Zero-Shot Event Detection.†Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence.

[9] Jennifer Golbeck, Cristina Robles, Michon Edmondson, and Karen Turner(2011). “Predicting personality from twitter.†In Passat/socialcom 2011, Privacy, Security, Risk and Trust, 149–156.

View Full Article:

How to Cite

., J., Mary, V., ., P., ., A., ., K., ., G., & Garvit, V. (2018). Stress Analytical Modelling Based on People’s Views on Social Networks. International Journal of Engineering & Technology, 7(3.12), 466–473.
Received 2018-07-24
Accepted 2018-07-24
Published 2018-07-20