Analyzing the Impact of Body Shaming on Twitter: A Study Using Naive Bayes Classifier and Machine Learning
DOI:
https://doi.org/10.61978/digitus.v1i1.58Keywords:
Twitter, Naïve Bayes Classifier, Machine Learning Rapid Miner, Body shamingAbstract
Twitter is one type of social media that is still widely used today. However, it happens frequently for Twitter users to post remarks that tend to criticize other Twitter users. Twitter users routinely make nasty remarks regarding body shaming, which has detrimental impacts on the victims such as diminishing self-esteem, leading to depressive illnesses, and, more seriously, raising the chance of suicide. Body shaming is the practice of criticizing someone's physical attributes, such as being slim, overweight, or having a flat nose. This study will use the Naive Bayes Classifier approach to do sentiment analysis based on the actions of body shaming on Twitter. Based on the performance testing results of Accuracy, Precision, and Recall using Machine Learning Rapid Miner with an initial dataset of 1000 body shaming tweets and a test dataset of 329 tweets, the following results were obtained: Accuracy of 80.55%, positive Precision of 100%, negative Precision of 80.43%, positive Recall of 3.03%, and negative Recall of 100%. In the preprocessing stage, tokenization resulted in a word cloud with the top 5 words being "overweight" at 51%, "body shaming" at 20%, "thin" at 11%, "people" at 10%, and "eating" at 8%.
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