periakiva/semanticAnalysis
Analysis of sentiment resulting from public figure's statements on social media against his/her public approval ratings. Sentiment was extracted and classified using scarpped data, Naive Bayes classifier and linear SVM. Both classifiers were used and compared for benchmark purposes. Used Pandas, sklearn, and Python.
This tool helps you understand how public figures' social media statements influence their approval ratings. It takes social media posts and public approval data to analyze sentiment and show you the relationship between what a public figure says and how the public perceives them. It's designed for political strategists, campaign managers, or public relations professionals.
No commits in the last 6 months.
Use this if you need to quickly assess the impact of a public figure's social media communication on their popularity.
Not ideal if you need a real-time monitoring solution or require deep causal inference beyond sentiment correlation.
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Last pushed
Dec 26, 2017
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