Sentiment-Analysis-Twitter | use different feature sets and machine learning classifiers

by ayushoriginal Python Version: Current License: No License

X-Ray Key Features Code Snippets Community Discussions Vulnerabilities Install Support

kandi X-RAY | Sentiment-Analysis-Twitter Summary

Sentiment-Analysis-Twitter is a Python library. Sentiment-Analysis-Twitter has no bugs, it has no vulnerabilities and it has low support. However Sentiment-Analysis-Twitter build file is not available. You can download it from GitHub.

:mortar_board:RESEARCH [NLP :thought_balloon:] We use different feature sets and machine learning classifiers to determine the best combination for sentiment analysis of twitter.

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Quality

Security

License

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Support

Sentiment-Analysis-Twitter has a low active ecosystem.

It has 690 star(s) with 268 fork(s). There are 45 watchers for this library.

It had no major release in the last 6 months.

There are 6 open issues and 0 have been closed. There are 1 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of Sentiment-Analysis-Twitter is current.

Quality

Sentiment-Analysis-Twitter has 0 bugs and 0 code smells.

Security

Sentiment-Analysis-Twitter has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

Sentiment-Analysis-Twitter code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

Sentiment-Analysis-Twitter 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

Sentiment-Analysis-Twitter releases are not available. You will need to build from source code and install.

Sentiment-Analysis-Twitter 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 Sentiment-Analysis-Twitter and discovered the below as its top functions. This is intended to give you an instant insight into Sentiment-Analysis-Twitter implemented functionality, and help decide if they suit your requirements.

Train and classify the tweets
Get training and test and test data .
Main function .
Print preprocessing statistics .
download twitter data from fetch list
Builds a csv output .
Convert old Stats to TSV format .
Compute the principal components of the training set .
Print a summary of the features stats .
Purge all tweet ids that have already been downloaded

Get all kandi verified functions for this library.

Sentiment-Analysis-Twitter Key Features

No Key Features are available at this moment for Sentiment-Analysis-Twitter.

Sentiment-Analysis-Twitter Examples and Code Snippets

No Code Snippets are available at this moment for Sentiment-Analysis-Twitter.

Community Discussions

No Community Discussions are available at this moment for Sentiment-Analysis-Twitter.Refer to stack overflow page for discussions.

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install Sentiment-Analysis-Twitter

You can download it from GitHub.
You can use Sentiment-Analysis-Twitter 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

Microblogging today has become a very popular communication tool among Internet users. Millions of messages are appearing daily in popular web-sites that provide services for microblogging such as Twitter, Tumblr, Facebook. Authors of those messages write about their life, share opinions on variety of topics and discuss current issues. Because of a free format of messages and an easy accessibility of microblogging platforms, Internet users tend to shift from traditional communication tools (such as traditional blogs or mailing lists) to microblogging services. As more and more users post about products and services they use, or express their political and religious views, microblogging web-sites become valuable sources of people’s opinions and sentiments. Such data can be efficiently used for marketing or social studies.[1].

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