Document from the year 2024 in the subject Computer Sciences - Computational linguistics, grade: 10, VIT University (VIT), course: Computer Science, language: English, abstract: The fake news on social media and various other media is wide spreading and is a mat- ter of serious concern due to its ability to cause a lot of social and national damage with destructive impacts. A lot of research is already focused on detecting it. Here we take three data sets namely " fake news and real news", "ISOT" and "LIAR". We try to implement six machine learning models on these data sets and trying to find their accu- racy and precision. The models we uses are Decision Tree, Random Forest, Support vector machine, Naive Bayes, KNN and LSTM. WE use tools like python scikit learn and NLP. Python scikit library can be used for feature extraction and textual analysis. We tries to find out which model works best on which data keeping the complexity of the data in mind. We would like to find a perfect model for any of the regional language. But the constrain is the availability of good dataset . So we try to propose a new dataset.
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Taschenbuch. Etat : Neu. This item is printed on demand - Print on Demand Titel. Neuware -Document from the year 2024 in the subject Computer Sciences - Computational linguistics, grade: 10, VIT University (VIT), course: Computer Science, language: English, abstract: The fake news on social media and various other media is wide spreading and is a mat- ter of serious concern due to its ability to cause a lot of social and national damage with destructive impacts. A lot of research is already focused on detecting it. Here we take three data sets namely ¿ fake news and real news¿, ¿ISOT¿ and ¿LIAR¿. We try to implement six machine learning models on these data sets and trying to find their accu- racy and precision. The models we uses are Decision Tree, Random Forest, Support vector machine, Naive Bayes, KNN and LSTM. WE use tools like python scikit learn and NLP. Python scikit library can be used for feature extraction and textual analysis. We tries to find out which model works best on which data keeping the complexity of the data in mind. We would like to find a perfect model for any of the regional language. But the constrain is the availability of good dataset . So we try to propose a new dataset.Books on Demand GmbH, Überseering 33, 22297 Hamburg 64 pp. Englisch. N° de réf. du vendeur 9783389024041
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Taschenbuch. Etat : Neu. Druck auf Anfrage Neuware - Printed after ordering - Document from the year 2024 in the subject Computer Sciences - Computational linguistics, grade: 10, VIT University (VIT), course: Computer Science, language: English, abstract: The fake news on social media and various other media is wide spreading and is a mat- ter of serious concern due to its ability to cause a lot of social and national damage with destructive impacts. A lot of research is already focused on detecting it. Here we take three data sets namely ¿ fake news and real news¿, ¿ISOT¿ and ¿LIAR¿. We try to implement six machine learning models on these data sets and trying to find their accu- racy and precision. The models we uses are Decision Tree, Random Forest, Support vector machine, Naive Bayes, KNN and LSTM. WE use tools like python scikit learn and NLP. Python scikit library can be used for feature extraction and textual analysis. We tries to find out which model works best on which data keeping the complexity of the data in mind. We would like to find a perfect model for any of the regional language. But the constrain is the availability of good dataset . So we try to propose a new dataset. N° de réf. du vendeur 9783389024041
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Taschenbuch. Etat : Neu. A Study of Tackling Fake News with Machine Learning Approaches | Balamurugan Rengeswaran (u. a.) | Taschenbuch | Englisch | 2024 | GRIN Verlag | EAN 9783389024041 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu. N° de réf. du vendeur 129323657
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