Depression Analysis using Sentiment Analysis via Social Media


Authors : Vinayak K Hugar, Vidya Uttur.

Volume/Issue : Volume 4 - 2019, Issue 5 - May

Google Scholar : https://goo.gl/DF9R4u

Scribd : https://bit.ly/2QPx11O

Abstract : As one of the prime reason for suicides among the youths of this generation is depression. Our application helps the psychologists in identifying whether a person is depressed or not. Our application uses the data of person for (ex: social media posts, comments, articles ) posted by the him in social media. The data of the respective person has been gathered and forwarded to the machine learning model to predict the depression of the person. As the model applies sentimental analysis method to find out the type emotion for each comment and article which has been posted by the person on social media. We have used Decision Tree Algorithm for the classification of data. The purpose of using this algorithm is the accuracy and precision of results it provides for making prediction. Our application helps a person to conduct the initial test for themselves or the parents to let them know the state or level of depression.

Keywords : Depression Analysis, Machine Learning Model, Sentimental Analysis, Decision Tree Algorithm.

As one of the prime reason for suicides among the youths of this generation is depression. Our application helps the psychologists in identifying whether a person is depressed or not. Our application uses the data of person for (ex: social media posts, comments, articles ) posted by the him in social media. The data of the respective person has been gathered and forwarded to the machine learning model to predict the depression of the person. As the model applies sentimental analysis method to find out the type emotion for each comment and article which has been posted by the person on social media. We have used Decision Tree Algorithm for the classification of data. The purpose of using this algorithm is the accuracy and precision of results it provides for making prediction. Our application helps a person to conduct the initial test for themselves or the parents to let them know the state or level of depression.

Keywords : Depression Analysis, Machine Learning Model, Sentimental Analysis, Decision Tree Algorithm.

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