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- W4386804950 abstract "The volume and scale of data being generated by social media is incrementing exponentially day by day. Private and public outlooks on various subjects or issues are expressed on social media. Sentiment analysis is a technique of examining and recognizing the sentiment of a statement that it embodies. Over the past decade, Twitter has emerged as a global leader in the social media industry, where people from around the world share their opinions and thoughts on events that occur all across the world. Sentiment analysis is a process of recognizing the sentiment of the Twitter data (tweets) conveyed by the user. In this study, we provide a survey and comparative analysis of existing techniques for opinion mining. This research paper mentions the pre-processing steps performed on the raw twitter data, including removing URLs, usernames, emojis, and stop words, and stemming to produce morphological variants of base words. Word clouds and frequency distributions of hashtags used in Racial and Non-Racial tweets were generated. The Bag of Words model was used for feature extraction, which involved converting text into a numerical matrix. The study used several classification models, including Multinomial Naive Bayes, Support Vector Machines, Logistic Regression, and K-Nearest Neighbors. The text also briefly explains Multinomial Naive Bayes and Support Vector Machines algorithms." @default.
- W4386804950 created "2023-09-17" @default.
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- W4386804950 date "2023-01-01" @default.
- W4386804950 modified "2023-09-27" @default.
- W4386804950 title "Identification of Racial Propaganda in Tweets Using Sentimental Analysis Models: A Comparative Study" @default.
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- W4386804950 doi "https://doi.org/10.1007/978-981-99-3716-5_28" @default.
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