Sentiment Classification on Women Safety across Indian Cities Based on Twitter Data using NLP and Machine Learning
RANJITHA, Pradeep Nayak, Vedanth M, Mahantesh G, Namitha D
Twitter, Sentiment analysis (SA), Machine Learning (ML), Random Forest, Naive Bayes (NB), Support Vector Machine (SVM), Women, Safety, Hash tag.
These days women are experiencing lots of violence such as persecution in places in several cities. This starts from stalking which then leads to abusive harassment or also called abuse assault. In this paper we mainly Primarily focus on the role of social media which can be utilized to advance the safety of women in India, given the unique reference to the cooperation of numerous virtual entertainment sites or applications like Twitter, Facebook and Instagram platforms. The proposed work focuses on developing a ML model that can determine the least safe states in India based on the sentiment classification of twitter data. Tweet from the Twitter application contains the text messages, audio data, video data, images, smiley expressions and hash-tags. This tweet content can be used for sentiment classification in NLP. The proposed work also implies the comparative study of four ML algorithms used in classification and the best accurate algorithm is used for analysis. Applications which include hash-tags such as metoo has been considered for the study. Machine learning algorithms including SVM, Random Forest, NB and Logistic Regression are analyzed.
Article Details
Unique Paper ID: 156041

Publication Volume & Issue: Volume 9, Issue 2

Page(s): 583 - 587
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