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Depression Level Detection Using CNN Approach from Tweet Data

EasyChair Preprint no. 4742

7 pagesDate: December 12, 2020


Sentiment analysis is the source of investigation that detects the emotion of people. It also analyzes the judgments, evaluations, and attitudes from the recorded expression. In natural language processing sentiment analysis is the largest effective field of data mining. The purpose of this investigation is to detect the depressed and non-depressed user from the social media which platform is Twitter. After detecting the depressed users from the sentiments, we can counsel those who have the probability of depression to provide proper treatment. This detection has been done by the Machine learning and Deep learning method Support Vector Machine and Convolutional Neural Network.

Keyphrases: DeepLearning, Depression, MachineLearning, social media

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {Lutfun Nahar and Fahima Alam and Khadiza Sultana Fairose},
  title = {Depression Level Detection Using CNN Approach from Tweet Data},
  howpublished = {EasyChair Preprint no. 4742},

  year = {EasyChair, 2020}}
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