TweetSets | Service for creating Twitter datasets for research | Dataset library

by gwu-libraries Python Version: v2.1.0 License: MIT

X-Ray Key Features Code Snippets Community Discussions(1)Vulnerabilities Install Support

kandi X-RAY | TweetSets Summary

TweetSets is a Python library typically used in Artificial Intelligence, Dataset applications. TweetSets 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.

Twitter datasets for research and archiving. TweetSets allows users to (1) select from existing datasets; (2) limit the dataset by querying on keywords, hashtags, and other parameters; (3) generate and download dataset derivatives such as the list of tweet ids and mention nodes/edges.

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Quality

Security

License

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Support

TweetSets has a low active ecosystem.

It has 16 star(s) with 0 fork(s). There are 8 watchers for this library.

It had no major release in the last 12 months.

There are 37 open issues and 57 have been closed. On average issues are closed in 113 days. There are 1 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of TweetSets is v2.1.0

Quality

TweetSets has 0 bugs and 0 code smells.

Security

TweetSets has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

TweetSets code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

TweetSets 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

TweetSets releases are available to install and integrate.

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

Installation instructions, examples and code snippets are available.

Top functions reviewed by kandi - BETA

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

Render a dataset
Add a derivative to the database
Get a connection to the database
Add filenames
Displays a limited dataset
Add a new dataset
Add a source dataset
Write out all tweets
Update current state
Displays a list of datasets
Extract mentions from a table
Get the state of the tweet index
Extract mentions from a dataframe
Compute the partition for a given number of tweets
Create Celery task
Make a Spark DataFrame from a JSON file
Fetch tweets by screen name
Extract the number of tweets from a DataFrame
Create dataset parameters
Finds files in path
Fetch tweets by a mention screen name
Write all the mentions to disk
Update the mentions table
Extract columns from a DataFrame
Concatenate json files into a single file
Show statistics about datasets

Get all kandi verified functions for this library.

TweetSets Key Features

No Key Features are available at this moment for TweetSets.

TweetSets Examples and Code Snippets

No Code Snippets are available at this moment for TweetSets.

Community Discussions

Trending Discussions on TweetSets

Executing Scala file of a project under SBT on commandline or in a text editor?

QUESTION

Executing Scala file of a project under SBT on commandline or in a text editor?

Asked 2017-Sep-03 at 21:08

I am learning Scala with this coursera course task here that provides SBT file. I download its objsets.zip here. Then I unzip it end and enter into it and type sbt and then console. I try to load the file src/main/scala/objsets/TweetSet.scala on commandline but I am getting a lot of errors.

...

ANSWER

Answered 2017-Sep-03 at 21:08

:load copies the contents of a file into the REPL line by line. That means that you end up trying to define a package (which is not allowed in the REPL), and then you try to import things that aren't visible, etc. If you use :load on a file that has a format useable by the REPL, it will work. In most cases, this means replacing the package line(s) with imports.

There's no need to use :load anyway. sbt console will place you in a REPL that has the project on its classpath. sbt consoleQuick will place you in a REPL that only has the dependencies on the classpath.

For your second question, you are meant to use sbt as a background process. In your terminal emulator, you'll have one tab running vim on your files, and in the other tab, you'll have sbt. In the tab with sbt, you can run ~compile, which recompiles your code every time you save a file in Vim. This replicates how IDEs show compiler errors/warnings as you type.

Source https://stackoverflow.com/questions/46027760

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

Vulnerabilities

No vulnerabilities reported

Install TweetSets

Create data directories on a volume with adequate storage:. For HTTPS support, uncomment and configure the nginx-proxy container in docker-compose.yml.
Create data directories on a volume with adequate storage: mkdir -p /tweetset_data/redis mkdir -p /tweetset_data/datasets mkdir -p /tweetset_data/elasticsearch/esdata1 mkdir -p /tweetset_data/elasticsearch/esdata2 chown -R 1000:1000 /tweetset_data/elasticsearch
Create an esdata<number> directory for each ElasticSearch container.
On OS X, the redis and esdata<number> directories must be ugo+rwx.
Create a directory, to be named as you choose, where tweet data files will be stored for loading. mkdir /dataset_loading
Clone or download this repository: git clone https://github.com/gwu-libraries/TweetSets.git
Change to the docker directory: cd docker
Copy the example docker files: cp example.docker-compose.yml docker-compose.yml cp example.env .env
Edit .env. This file is annotated to help you select appropriate values.
Create dataset_list_msg.txt in the docker directory. The contents of this file will be displayed on the dataset list page. It can be used to list other datasets that are available, but not yet loaded. If leaving the file empty then: touch dataset_list_msg.txt
Bring up the containers: docker-compose up -d
Clusters must have at least a primary node and two additional nodes. For HTTPS support, uncomment and configure the nginx-proxy container in docker-compose.yml.
Create data directories on a volume with adequate storage: mkdir -p /tweetset_data/redis mkdir -p /tweetset_data/datasets mkdir -p /tweetset_data/full_datasets mkdir -p /tweetset_data/elasticsearch chown -R 1000:1000 /tweetset_data/elasticsearch
Create a directory, to be named as you choose, where tweet data files will be stored for loading. mkdir /dataset_loading
Clone or download this repository: git clone https://github.com/gwu-libraries/TweetSets.git
Change to the docker directory: cd docker
Copy the example docker files: cp example.cluster-primary.docker-compose.yml docker-compose.yml cp example.env .env
Edit .env. This file is annotated to help you select appropriate values.
Create dataset_list_msg.txt in the docker directory. The contents of this file will be displayed on the dataset list page. It can be used to list other datasets that are available, but not yet loaded. If leaving the file empty then: touch dataset_list_msg.txt
Bring up the containers: docker-compose up -d
Create data directories on a volume with adequate storage: mkdir -p /tweetset_data/elasticsearch chown -R 1000:1000 /tweetset_data/elasticsearch
Clone or download this repository: git clone https://github.com/gwu-libraries/TweetSets.git
Change to the docker directory: cd docker
Copy the example docker files: cp example.cluster-node.docker-compose.yml docker-compose.yml cp example.cluster-node.env .env
Edit .env. This file is annotated to help you select appropriate values. Note that 2 cluster nodes must have MASTER set to true.
Bring up the containers: docker-compose up -d

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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