SemEval-2016 | Twitter Sentiment System for SemEval

by WladimirSidorenko Python Version: Current License: MIT

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

kandi X-RAY | SemEval-2016 Summary

SemEval-2016 is a Python library. SemEval-2016 has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has low support. You can download it from GitHub.

TRAINING + DEV dataset for SemEval-2016 Task 4. Version 1.0: October 15, 2015. data/train/src/100_topics_100_tweets.topic-two-point.subtask-BD.train.txt -- training input for subtasks B and D data/train/src/100_topics_100_tweets.topic-five-point.subtask-CE.train.txt -- training input for subtasks C and E. data/dev/src/100_topics_100_tweets.topic-two-point.subtask-BD.dev.txt -- dev input for subtasks B and D data/dev/src/100_topics_100_tweets.topic-five-point.subtask-CE.dev.txt -- dev input for subtasks C and E.

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SemEval-2016 has a low active ecosystem.

It has 15 star(s) with 8 fork(s). There are 2 watchers for this library.

It had no major release in the last 6 months.

SemEval-2016 has no issues reported. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of SemEval-2016 is current.

Quality

SemEval-2016 has 0 bugs and 0 code smells.

Security

SemEval-2016 has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

SemEval-2016 code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

SemEval-2016 is licensed under the MIT License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

Reuse

SemEval-2016 releases are not available. You will need to build from source code and install.

Build file is available. You can build the component from source.

Installation instructions are not available. Examples and code snippets are available.

SemEval-2016 saves you 614 person hours of effort in developing the same functionality from scratch.

It has 1428 lines of code, 67 functions and 6 files.

It has high code complexity. Code complexity directly impacts maintainability of the code.

Top functions reviewed by kandi - BETA

kandi has reviewed SemEval-2016 and discovered the below as its top functions. This is intended to give you an instant insight into SemEval-2016 implemented functionality, and help decide if they suit your requirements.

Preprocess a pre - trained model
Digitize the feature vocabulary
Convert a set of features to TS
Cleanup shared variables
Iterate over lines in a file
Strips comments
Safely replace a_re
Get fields from a line
Compute statistics for each class
Compute statistics for a character n - gram
Compute the statistics for each character
Compute the mean and variance of a statistic
Train the model
Activate the predict function
2nd layer
Estimate embeddings for a given corpus
Debugging function
Output embeddings
Debugging function for debugging
Computes the covariance matrix for each character
Calculate the correlation matrix
Evaluate a GLD file
Translate a tag
Compute the class and character statistics
Iterate through characters of a string

Get all kandi verified functions for this library.

SemEval-2016 Key Features

No Key Features are available at this moment for SemEval-2016.

SemEval-2016 Examples and Code Snippets

No Code Snippets are available at this moment for SemEval-2016.

Community Discussions

No Community Discussions are available at this moment for SemEval-2016.Refer to stack overflow page for discussions.

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

Vulnerabilities

No vulnerabilities reported

Install SemEval-2016

You can download it from GitHub.
You can use SemEval-2016 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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