Abstract

Since this pandemic has taken place the world is not in a great condition physically and mentally. People are witnessing all kinds of unexpected circumstances. Under such circumstances, people tend to break down. Mental health is like a catalyst to physical health; it can either boost up or tank the entire recovery mechanism of a person. Everyone should have a healthy mindset toward life. Mood Trend is a system which aids users' mindset or mood with small amounts of positive reinforcements. In this way, we make a significant impact on users' lives in the long run. The website will detect the sentiment behind the user's words and provide content to enhance their mood. The user can view this content material to lighten his/her temper or to control stress. Mood Trend also has an option where the users can upload a picture of themselves and the system will detect sentiment.

References

• S. Poria, D. Hazarika, N. Majumder, G. Naik, R. Mihalcea, E. Cambria. MELD: A Multimodal Multi-Party Dataset for Emotion Recognition in Conversation. (2018)
• Godbole, Namrata & Srinivasaiah, Manjunath & Skiena, Steven. (2007). Large-Scale Sentiment Analysis for News and Blogs. ICWSM 2007 - International Conference on Weblogs and Social Media.
• Diana Maynard and Mark Greenwood. 2014. Who cares about Sarcastic Tweets? Investigating the Impact of Sarcasm on Sentiment Analysis.. In Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14), pages 4238–4243, Reykjavik, Iceland. European Language Resources Association (ELRA).
• Chan, Leslie & Martens, Bob. (2007). Openness in Digital Publishing: Awareness, Discovery and Access - Proceedings of the 11th International Conference on Electronic Publishing held in Vienna - ELPUB 2007, Vienna, Austria, June 13-15, 2007. Proceedings. 10.13140/2.1.4998.9927.
• Syahputra, Harry & Basyar, L & Tamba, A. (2020). Sentiment Analysis of Public Opinion on The Go-Jek Indonesia Through Twitter Using Algorithm Support Vector Machine. Journal of Physics: Conference Series. 1462. 012063. 10.1088/1742-6596/1462/1/012063.
• Shifeng Zhang (NLPR, CASIA, UCAS, China) , Xiaobo Wang (JD AI Research), Ajian Liu (MUST, Macau, China), Chenxu Zhao (JD AI Research), Jun Wan (NLPR, CASIA, UCAS, China), Sergio Escalera (University of Barcelona), Hailin Shi (JD AI Research), Zezheng Wang (JD Finance), Stan Z. Li (NLPR, CASIA, UCAS, China).
• Song, Yading & Dixon, Simon & Pearce, Marcus. (2012). A Survey of Music Recommendation Systems and Future Perspectives.
• Shivhare, Shiv Naresh & Khethawat, Saritha. (2012). Emotion Detection from Text. Computer Science & Information Technology. 2. 10.5121/csit.2012.2237.
• Voggu, Suman Venkata Sai & Champawat, Yuvraj & Tripathy, B.K.. (2019). Recommendation System Based on Text Analysis. 10.35940/ijitee.I8626.078919.
• Ji, Zhenyan & Pi, Huaiyu & Wei, Wei & Xiong, Bo & Woźniak, Marcin & Damasevicius, Robertas. (2019). Recommendation Based on Review Texts and Social Communities: A Hybrid Model. IEEE Access. 7. 40416-40427. 10.1109/ACCESS.2019.2897586.