Sentimental Analysis of Sexual Harassment as a Health and Development Issue Using Machine Learning Techniques

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Abstract

People share their opinions on different Social media platforms. Twitter is one of these where people can share their ideas, opinions on a topic, and life experiences with limited characters. In this paper, the Twitter dataset of the #MeToo hashtag from the year 2017 to 2020 is used to analyze the sentiments of people about women’s issues which are very critical issues in our society so, a total of 1,15,575 tweets are collected from the data world website and also collected Twitter scraper using the python programming language and then applied Text Blob tool to labeled tweets as positive, negative and neutral and after that build the model using machine learning techniques. Four different classifiers Support Vector Machine, Naive Bayes, Logistic Regression, and Random Forest Classifier were implemented to analyze sentiment efficiently. Further, compared these four models had been done to prove highly effective and accurate based on the analysis of feelings and opinions regarding women's issues on the #MeToo hashtag.

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Keywords

Development, #MeToo; Machine Learning Techniques; Sentiment Analysis; Sexual Harassment,Twitter; Women Health DoE