9786207476435 - emotions recognition in textual tweets by machine learning algorithms di burri, rama devi (11 risultati)
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Da: Ria Christie Collections, Uxbridge, Regno UnitoRia Christie Collections
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Da: Books Puddle, New York, NY, U.S.A.Books Puddle
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Da: preigu, Osnabrück, Germaniapreigu
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Taschenbuch. Condizione: Neu. Emotions Recognition in Textual Tweets by Machine Learning Algorithms | Rama Devi Burri | Taschenbuch | Englisch | 2024 | LAP LAMBERT Academic Publishing | EAN 9786207476435 | Verantwortliche Person für die EU: SIA OmniScriptum Publishing, Brivibas Gatve 197, 1039 RIGA, LETTLAND, customerservice[at]…vdm-vsg[dot]de | Anbieter: preigu.
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Da: PBShop.store US, Wood Dale, IL, U.S.A.PBShop.store US
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PAP. Condizione: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.
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Da: PBShop.store UK, Fairford, GLOS, Regno UnitoPBShop.store UK
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EUR 50,86
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PAP. Condizione: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.
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Da: Majestic Books, Hounslow, Regno UnitoMajestic Books
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EUR 60,86
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Condizione: New. Print on Demand.
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Da: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, GermaniaBuchWeltWeit Ludwig Meier e.K.
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Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware 52 pp. Englisch.
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Da: Biblios, frankfurt am main, HESSE, GermaniaBiblios
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Condizione: New. PRINT ON DEMAND.
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Da: moluna, Greven, Germaniamoluna
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Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. In this book is proposed an emotion recognition system where it recognizes emotions in tweets. Emotions play a vital role in our lives. As we can see that many people use social media where they use the platform for…many purposes, some of them tweet in a go.
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Da: buchversandmimpf2000, Emtmannsberg, BAYE, Germaniabuchversandmimpf2000
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Taschenbuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -In this book is proposed an emotion recognition system where it recognizes emotions in tweets. Emotions play a vital role in our lives. As we can see that many people use social media where they use the platform for many purposes, some… of them tweet in a good way and some of them in a bullying way. Emotions and opinions of different people can be carried out on tweets to analyze public opinion on a news and social events that take place in present society. By using machine learning algorithms we have implemented emotion recognition by classifying tweets as positive and negative. By recognizing these positive and negative tweets we can identify people emotions where we can reduce the forged statements. Initially authors have divided the dataset into train and test dataset, where it is used to train the model and by comparing the train data with the test data, the model recognizes the emotions in tweets. By using SVM and naïve bayes algorithms we classify the text based on twitter into different emotions and predicted emojis like love, fear, anger, sadness, joy. Based on the performance analysis we predicted optimal result with accuracy and F1 score.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 52 pp. Englisch.
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Da: AHA-BUCH GmbH, Einbeck, GermaniaAHA-BUCH GmbH
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EUR 44,59
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Taschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - In this book is proposed an emotion recognition system where it recognizes emotions in tweets. Emotions play a vital role in our lives. As we can see that many people use social media where they use the platform for many purposes, some…of them tweet in a good way and some of them in a bullying way. Emotions and opinions of different people can be carried out on tweets to analyze public opinion on a news and social events that take place in present society. By using machine learning algorithms we have implemented emotion recognition by classifying tweets as positive and negative. By recognizing these positive and negative tweets we can identify people emotions where we can reduce the forged statements. Initially authors have divided the dataset into train and test dataset, where it is used to train the model and by comparing the train data with the test data, the model recognizes the emotions in tweets. By using SVM and naïve bayes algorithms we classify the text based on twitter into different emotions and predicted emojis like love, fear, anger, sadness, joy. Based on the performance analysis we predicted optimal result with accuracy and F1 score.
