Analytical Study of Social Media Sentiment and Engagement Pattern using Machine Learning Approach
Keywords:
Engagement analysis, Machine learning, Network analysis, Sentiment analysis, Social media analyticsAbstract
Nowadays, millions of posts are published on well-known social media platforms by users. Every post that is input by the user has different reactions in terms of likes, shares, and comments from people. Some posts have created a greater impact on people, and some have not. But some sectors like business and marketing, politics, news, etc. are eager to take advantage of social media posts. Priority-wise, they are thinking about recording reactions by the people on multiple posts created by the user. This study helps to gain insight into people’s reactions to specific posts in terms of likes, shares and comments to know the actual sentiments of people against this post and their analysis in terms of engagement, i.e., posts of positive, negative and neutral nature for the people. This project analyzes the text of social media posts along with user engagement metrics, including likes, shares, and comments. Using machine learning and sentiment analysis techniques, the system classifies each post as positive, negative, or neutral and evaluates the corresponding user engagement.
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