Abstract:
Sentiment and emotion analysis is the way of discovering positive, negative and neutral
sentiment in text. In our paper we try to analyze the sentiment from posts/comments in
psychological support groups in the most popular social media platform Facebook. The
advantage of social media is it’s not only used for entertainment but also for helping
people who need help. Nowadays many people suffer from depression, it's one of the
most serious mental health problems. psychological support groups on Facebook try to
help those depressed people. We analyze many posts/comments on psychological support
groups on Facebook and predict how much those posts/comments can support or help a
person. In our project we collect practical examples that are posted in different
psychological groups, and we calculate those posts as helping people to improve their
situation. We can know the effect of how supportive these posts are or not and how this
post is helping people. Is it in position, positive or neutral way? For this task we divided
the complete work into two sections: sentiment detection and analyzing the ability to
detect sentiment from such a special category of texts. For visualization here we use
Matplotlib, Seaborn, NumPy. For graph visualization we use scatterplot, word cloud and
for visualization we bring word cloud from monkey learning website. For overall tasks
we have utilized Natural Language Toolkit (NLTK) and TextBlob, which are publicly
available python packages.
