Twitter-Sentiment-Analysis---Analytics-Vidhya | Problem Statement The objective of this task
kandi X-RAY | Twitter-Sentiment-Analysis---Analytics-Vidhya Summary
kandi X-RAY | Twitter-Sentiment-Analysis---Analytics-Vidhya Summary
Twitter-Sentiment-Analysis---Analytics-Vidhya is a Python library typically used in Telecommunications, Media, Advertising, Marketing applications. Twitter-Sentiment-Analysis---Analytics-Vidhya has no vulnerabilities and it has low support. However Twitter-Sentiment-Analysis---Analytics-Vidhya has 1 bugs and it build file is not available. You can download it from GitHub.
Problem Statement The objective of this task is to detect hate speech in tweets. For the sake of simplicity, we say a tweet contains hate speech if it has a racist or sexist sentiment associated with it. So, the task is to classify racist or sexist tweets from other tweets. Formally, given a training sample of tweets and labels, where label '1' denotes the tweet is racist/sexist and label '0' denotes the tweet is not racist/sexist, your objective is to predict the labels on the test dataset. Motivation Hate speech is an unfortunately common occurrence on the Internet. Often social media sites like Facebook and Twitter face the problem of identifying and censoring problematic posts while weighing the right to freedom of speech. The importance of detecting and moderating hate speech is evident from the strong connection between hate speech and actual hate crimes. Early identification of users promoting hate speech could enable outreach programs that attempt to prevent an escalation from speech to action. Sites such as Twitter and Facebook have been seeking to actively combat hate speech. In spite of these reasons, NLP research on hate speech has been very limited, primarily due to the lack of a general definition of hate speech, an analysis of its demographic influences, and an investigation of the most effective features. Data Our overall collection of tweets was split in the ratio of 65:35 into training and testing data. Out of the testing data, 30% is public and the rest is private. Data Files train.csv - For training the models, we provide a labelled dataset of 31,962 tweets. The dataset is provided in the form of a csv file with each line storing a tweet id, its label and the tweet. There is 1 test file (public) test_tweets.csv - The test data file contains only tweet ids and the tweet text with each tweet in a new line.
Problem Statement The objective of this task is to detect hate speech in tweets. For the sake of simplicity, we say a tweet contains hate speech if it has a racist or sexist sentiment associated with it. So, the task is to classify racist or sexist tweets from other tweets. Formally, given a training sample of tweets and labels, where label '1' denotes the tweet is racist/sexist and label '0' denotes the tweet is not racist/sexist, your objective is to predict the labels on the test dataset. Motivation Hate speech is an unfortunately common occurrence on the Internet. Often social media sites like Facebook and Twitter face the problem of identifying and censoring problematic posts while weighing the right to freedom of speech. The importance of detecting and moderating hate speech is evident from the strong connection between hate speech and actual hate crimes. Early identification of users promoting hate speech could enable outreach programs that attempt to prevent an escalation from speech to action. Sites such as Twitter and Facebook have been seeking to actively combat hate speech. In spite of these reasons, NLP research on hate speech has been very limited, primarily due to the lack of a general definition of hate speech, an analysis of its demographic influences, and an investigation of the most effective features. Data Our overall collection of tweets was split in the ratio of 65:35 into training and testing data. Out of the testing data, 30% is public and the rest is private. Data Files train.csv - For training the models, we provide a labelled dataset of 31,962 tweets. The dataset is provided in the form of a csv file with each line storing a tweet id, its label and the tweet. There is 1 test file (public) test_tweets.csv - The test data file contains only tweet ids and the tweet text with each tweet in a new line.
Support
Quality
Security
License
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Support
Twitter-Sentiment-Analysis---Analytics-Vidhya has a low active ecosystem.
It has 5 star(s) with 5 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 Twitter-Sentiment-Analysis---Analytics-Vidhya is current.
Quality
Twitter-Sentiment-Analysis---Analytics-Vidhya has 1 bugs (0 blocker, 0 critical, 1 major, 0 minor) and 7 code smells.
Security
Twitter-Sentiment-Analysis---Analytics-Vidhya has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
Twitter-Sentiment-Analysis---Analytics-Vidhya code analysis shows 0 unresolved vulnerabilities.
There are 0 security hotspots that need review.
License
Twitter-Sentiment-Analysis---Analytics-Vidhya 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
Twitter-Sentiment-Analysis---Analytics-Vidhya releases are not available. You will need to build from source code and install.
Twitter-Sentiment-Analysis---Analytics-Vidhya has no build file. You will be need to create the build yourself to build the component from source.
It has 297 lines of code, 3 functions and 2 files.
It has low code complexity. Code complexity directly impacts maintainability of the code.
Top functions reviewed by kandi - BETA
kandi has reviewed Twitter-Sentiment-Analysis---Analytics-Vidhya and discovered the below as its top functions. This is intended to give you an instant insight into Twitter-Sentiment-Analysis---Analytics-Vidhya implemented functionality, and help decide if they suit your requirements.
- Extract the hashtags from a tweet .
- Removes matching pattern from input_txt .
Get all kandi verified functions for this library.
Twitter-Sentiment-Analysis---Analytics-Vidhya Key Features
No Key Features are available at this moment for Twitter-Sentiment-Analysis---Analytics-Vidhya.
Twitter-Sentiment-Analysis---Analytics-Vidhya Examples and Code Snippets
No Code Snippets are available at this moment for Twitter-Sentiment-Analysis---Analytics-Vidhya.
Community Discussions
No Community Discussions are available at this moment for Twitter-Sentiment-Analysis---Analytics-Vidhya.Refer to stack overflow page for discussions.
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install Twitter-Sentiment-Analysis---Analytics-Vidhya
You can download it from GitHub.
You can use Twitter-Sentiment-Analysis---Analytics-Vidhya 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 Twitter-Sentiment-Analysis---Analytics-Vidhya 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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