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- W4200205887 abstract "The use of social media has grown rapidly in the last decades. Many of these social media platforms, i.e., Twitter, do not screen or remove potentially offensive/ harmful tweets. It can cause discomfort for the users, especially if minors encounter these words while they are surfing the media. We came up with a profanity detection model with text mining that uses bag of words algorithm. Then we applied four different classifiers to see which one gets the highest accuracy score. The results show a high accuracy performance, with Random Forest classification being the highest with 97.5%." @default.
- W4200205887 created "2021-12-31" @default.
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- W4200205887 date "2021-09-23" @default.
- W4200205887 modified "2023-09-26" @default.
- W4200205887 title "Detecting Racist and Bad Words Using Text Mining in Social Media" @default.
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- W4200205887 doi "https://doi.org/10.1109/icitacee53184.2021.9617530" @default.
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