Abstract:
Depression is an acute problem throughout the world, where more than 264 million people are
suffering from it. Due to worst and prolong depression near around 800000 people dies in every
year. The real problem is that most of the people are not concern of the fact that they are suffering
from depression. Here, our aim was to find out whether an individual is in depression or not by
analyzing social media text information. Our dataset consists of 1500 sentences, which was
collected from different social media platforms– Facebook, Tweeter, and Instagram. Then we have
performed some data preprocessing approaches such as– tokenization, remove of stop words,
remove of empty string, remove of punctuations, stemming and lemmatizing. After data
preprocessing, we considered processed text as input. We work on six different machine learning
classifiers which produced great accuracy over our dataset. Among six algorithms, Multinomial
Naive Bayes and Logistic Regression provided 95% accuracy.
