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- W2997280166 abstract "Twitter is an online social networking site where people can write short messages called tweets to express their emotions on any issue. In the existing system, classification of the tweets has been done in two main categories (positive and negative) using Naive Bayes classifier and also categorized into three main categories (i.e., positive, negative and neutral). For classification, they have used “Random Forest” classifier. Dataset consists of tweets for training and testing, which is collected manually. In the proposed approach, the classification of the tweets is doing in three main categories (i.e., positive, negative and neutral). In this approach, we are classifying tweets into positive, negative and neutral class, and for classification, we are presenting a hybrid approach of Naive Bayes and K-Nearest Neighbor (KNN) classifiers. Dataset consists of 21,000 tweets which are taken by using Twitter API, and performance is calculated in terms of accuracy of tweet classification." @default.
- W2997280166 created "2020-01-10" @default.
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- W2997280166 date "2020-01-01" @default.
- W2997280166 modified "2023-09-24" @default.
- W2997280166 title "Text Categorization Using Sentiment Analysis" @default.
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- W2997280166 doi "https://doi.org/10.1007/978-981-15-0790-8_35" @default.
- W2997280166 hasPublicationYear "2020" @default.
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