bayesianfakenewsclassifier | Twitter Fake News Classifier
kandi X-RAY | bayesianfakenewsclassifier Summary
kandi X-RAY | bayesianfakenewsclassifier Summary
bayesianfakenewsclassifier is a Python library. bayesianfakenewsclassifier has no bugs, it has no vulnerabilities and it has low support. However bayesianfakenewsclassifier build file is not available. You can download it from GitHub.
Twitter Fake News Classifier (Bayes Theorem) The use of the Internet as a means of accessing news content has become part of the daily lives of most of its users. In particular, social networks, driven by the technological advancement of mobile Internet and mobile devices, have become major sources of information. With the popularity of such social networks, the spread of fake news has increased considerably in recent years. Such news are called fake news, which have a structure similar to a real news, and are created in order to gain advantages in various sectors, such as politics, sports, among others. One of the main social networks today is Twitter, which has millions of active users and allows the collection of information from its database through libraries specific for this purpose. From the data collected from this social network, it is possible to analyze objects of these data through various algorithms in the computer literature, among which is the Naive Bayes classifier, commonly used in the classification of textual documents. In this sense, this project aims to present a classifier capable of detecting fake news, having as input a database composed of news collected on the social network Twitter. Initially, we collected and stored the tweets that will be analyzed and, from then on, the Naive Bayes classifier was used in the experiment that determined the veracity of the information, such experiment obtained 85% maximum accuracy. With the results of this classification step, empirical analyzes were performed in order to verify if these results approximate the labeled value. Thus, quantitative and qualitative analyzes were performed, in which it was possible to infer the correlation of the results obtained with reality.
Twitter Fake News Classifier (Bayes Theorem) The use of the Internet as a means of accessing news content has become part of the daily lives of most of its users. In particular, social networks, driven by the technological advancement of mobile Internet and mobile devices, have become major sources of information. With the popularity of such social networks, the spread of fake news has increased considerably in recent years. Such news are called fake news, which have a structure similar to a real news, and are created in order to gain advantages in various sectors, such as politics, sports, among others. One of the main social networks today is Twitter, which has millions of active users and allows the collection of information from its database through libraries specific for this purpose. From the data collected from this social network, it is possible to analyze objects of these data through various algorithms in the computer literature, among which is the Naive Bayes classifier, commonly used in the classification of textual documents. In this sense, this project aims to present a classifier capable of detecting fake news, having as input a database composed of news collected on the social network Twitter. Initially, we collected and stored the tweets that will be analyzed and, from then on, the Naive Bayes classifier was used in the experiment that determined the veracity of the information, such experiment obtained 85% maximum accuracy. With the results of this classification step, empirical analyzes were performed in order to verify if these results approximate the labeled value. Thus, quantitative and qualitative analyzes were performed, in which it was possible to infer the correlation of the results obtained with reality.
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Support
bayesianfakenewsclassifier has a low active ecosystem.
It has 2 star(s) with 0 fork(s). There are 1 watchers for this library.
It had no major release in the last 6 months.
bayesianfakenewsclassifier has no issues reported. There are no pull requests.
It has a neutral sentiment in the developer community.
The latest version of bayesianfakenewsclassifier is current.
Quality
bayesianfakenewsclassifier has no bugs reported.
Security
bayesianfakenewsclassifier has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
License
bayesianfakenewsclassifier does not have a standard license declared.
Check the repository for any license declaration and review the terms closely.
Without a license, all rights are reserved, and you cannot use the library in your applications.
Reuse
bayesianfakenewsclassifier releases are not available. You will need to build from source code and install.
bayesianfakenewsclassifier has no build file. You will be need to create the build yourself to build the component from source.
Top functions reviewed by kandi - BETA
kandi has reviewed bayesianfakenewsclassifier and discovered the below as its top functions. This is intended to give you an instant insight into bayesianfakenewsclassifier implemented functionality, and help decide if they suit your requirements.
- Preprocessamento text .
- Removes accentuation from the given string .
- Compute the media of a vector .
Get all kandi verified functions for this library.
bayesianfakenewsclassifier Key Features
No Key Features are available at this moment for bayesianfakenewsclassifier.
bayesianfakenewsclassifier Examples and Code Snippets
No Code Snippets are available at this moment for bayesianfakenewsclassifier.
Community Discussions
No Community Discussions are available at this moment for bayesianfakenewsclassifier.Refer to stack overflow page for discussions.
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install bayesianfakenewsclassifier
You can download it from GitHub.
You can use bayesianfakenewsclassifier like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.
You can use bayesianfakenewsclassifier like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.
Support
For any new features, suggestions and bugs create an issue on GitHub.
If you have any questions check and ask questions on community page Stack Overflow .
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