Learning-Sentiment-Specific-Word-Embedding-for-Tweets | project done by Montassar Ajam
kandi X-RAY | Learning-Sentiment-Specific-Word-Embedding-for-Tweets Summary
kandi X-RAY | Learning-Sentiment-Specific-Word-Embedding-for-Tweets Summary
Learning-Sentiment-Specific-Word-Embedding-for-Tweets is a Python library. Learning-Sentiment-Specific-Word-Embedding-for-Tweets has no bugs, it has no vulnerabilities and it has low support. However Learning-Sentiment-Specific-Word-Embedding-for-Tweets build file is not available. You can download it from GitHub.
A project done by Montassar Ajam/ Mounir Ouled Ltaief and Aziz ben Othmane
A project done by Montassar Ajam/ Mounir Ouled Ltaief and Aziz ben Othmane
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Learning-Sentiment-Specific-Word-Embedding-for-Tweets has a low active ecosystem.
It has 4 star(s) with 1 fork(s). There are 2 watchers for this library.
It had no major release in the last 6 months.
There are 1 open issues and 0 have been closed. There are no pull requests.
It has a neutral sentiment in the developer community.
The latest version of Learning-Sentiment-Specific-Word-Embedding-for-Tweets is current.
Quality
Learning-Sentiment-Specific-Word-Embedding-for-Tweets has no bugs reported.
Security
Learning-Sentiment-Specific-Word-Embedding-for-Tweets has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
License
Learning-Sentiment-Specific-Word-Embedding-for-Tweets 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.
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Learning-Sentiment-Specific-Word-Embedding-for-Tweets releases are not available. You will need to build from source code and install.
Learning-Sentiment-Specific-Word-Embedding-for-Tweets 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 Learning-Sentiment-Specific-Word-Embedding-for-Tweets and discovered the below as its top functions. This is intended to give you an instant insight into Learning-Sentiment-Specific-Word-Embedding-for-Tweets implemented functionality, and help decide if they suit your requirements.
- Process tweets from csv file
- Get a feature vector from a tweet
- Returns a list of stop words
- Process Tweet
- Replace two characters
- Create a tf trainer
- Creates a trainer
- Return a function that returns a function that saves the model and vectors
- Create the SSW model
- Build a Word embedding feature vectorizer
- Build a word vector from a list of tokens
Get all kandi verified functions for this library.
Learning-Sentiment-Specific-Word-Embedding-for-Tweets Key Features
No Key Features are available at this moment for Learning-Sentiment-Specific-Word-Embedding-for-Tweets.
Learning-Sentiment-Specific-Word-Embedding-for-Tweets Examples and Code Snippets
No Code Snippets are available at this moment for Learning-Sentiment-Specific-Word-Embedding-for-Tweets.
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Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install Learning-Sentiment-Specific-Word-Embedding-for-Tweets
You can download it from GitHub.
You can use Learning-Sentiment-Specific-Word-Embedding-for-Tweets 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 Learning-Sentiment-Specific-Word-Embedding-for-Tweets 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.
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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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