Affiliation:
1. MH Saboo Siddik College of Engineering, Byculla, Mumbai, India
Abstract
One of the popular media these days... Guess what? One WhatsApp. It is one of the favorite advertising platforms of all of us because of its attractive features. It has more than 2 billion users worldwide and "according to research, the average user spends more than 195 minutes per week on WhatsApp”[10]. How bad is the above. We are happy that social media, where information can be accessed and updated more easily, is in our lives. However, no one can deny that its excessive use can lead to serious health problems that can affect our lives. Being addicted can affect the way you live, work, and sleep. Addiction reduced the ability to listen, think, and reason, leading to negative emotions.[1] Therefore, in order to reduce such problems, our aim is to create an interactive system that can track ourselves and detect when we use WhatsApp. We will build a web application using various methods available for business analysis. It is a combination of machine learning and NLP. This WhatsApp chat analyzer retrieves and analyzes WhatsApp chat files from users and gives different results in form of visualizations.
Reference11 articles.
1. Ahmed, I., Fiaz, T., Mobile phone to youngsters: Necessity or addiction, African Journal of Business Management Vol.5 (32), pp. 12512-12519, Aijaz, K. (2011).
2. Aharony, N., T., G., The Importance of the WhatsApp Family Group: An Exploratory Analysis. Aslib Journal of Information Management, Vol. 68, Issue 2, pp.1-37 (2016).
3. Access Data Corporation. FTK Imager, 2013. Available at http://www.accessdata.com/support/product-downloads.
4. WhatsApp Group Data Analysis with R. Available at
5. https://www.ijcaonline.org/archives/volume154/number4/patil-2016-ijca-912116.pdf