Detection of Depression and Mental illness of Twitter users using Machine Learning
M. Ambika1, K.V. Devakrishnan2, A. Divya3, R. Gowtham Raj4, K. Kaviyaa5
1M. Ambika*, Pursuing, (B.E) degree at Computer Science and Engineering in Sri Shakthi Institute of Engineering and Technology, Coimbatore.
2K.V. Devakrishnan, Pursuing, (B.E) degree at Computer Science and Engineering in Sri Shakthi Institute of Engineering and Technology, Coimbatore.
3A. Divya, Pursuing, (B.E) degree at Computer Science and Engineering in Sri Shakthi Institute of Engineering and Technology, Coimbatore.
4R. Gowtham Raj, Pursuing, (B.E) degree at Computer Science and Engineering in Sri Shakthi Institute of Engineering and Technology, Coimbatore.
5K. Kaviyaa, Pursuing, (B.E) degree at Computer Science and Engineering in Sri Shakthi Institute of Engineering and Technology, Coimbatore.
Manuscript received on March 30, 2020. | Revised Manuscript received on April 05, 2020. | Manuscript published on April 30, 2020. | PP: 1331-1335 | Volume-9 Issue-4, April 2020. | Retrieval Number: D8314049420/2020©BEIESP | DOI: 10.35940/ijeat.D8314.049420
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Today Micro-blogging has become a popular Internet-user communication tool. Millions of users exchange views on different aspects of their lives. Thus micro blogging websites are a rich source of opinion mining data or Sentiment Analysis (SA) information. Due to the recent emergence of micro blogging, there are a few research works devoted to this subject. We concentrate in our paper on Twitter, one of the prominent micro blogging sites to analyze sentiment of the public. We’ll demonstrate, how to gather real-time twitter data for sentiment analysis or opinion mining purposes, and employed algorithms like Term Frequency – Inverse Document Frequency (TF-IDF), Bag of Words (BOW) and Multinomial Naive Bayes ( MNB). We are able to determine positive and negative sentiments for the real-time twitter data using the above chosen algorithms. Experimental evaluations below shows that the algorithms used are efficient and it can be used as a application in detection of the depression of the people. We worked with English in this article, but for any other language it can be used.
Keywords: Machine Learning, Python, Sentiment analysis, Twitter.