https://github.com/dineshkarthik/tweetalyzer
Stream and Visualise Tweets on specific topics
https://github.com/dineshkarthik/tweetalyzer
tweets twitter-streaming-api
Last synced: about 1 year ago
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Stream and Visualise Tweets on specific topics
- Host: GitHub
- URL: https://github.com/dineshkarthik/tweetalyzer
- Owner: Dineshkarthik
- License: mit
- Created: 2017-03-23T10:17:15.000Z (about 9 years ago)
- Default Branch: master
- Last Pushed: 2023-05-01T21:36:59.000Z (about 3 years ago)
- Last Synced: 2025-03-30T21:51:15.404Z (about 1 year ago)
- Topics: tweets, twitter-streaming-api
- Language: Python
- Size: 26.4 KB
- Stars: 3
- Watchers: 2
- Forks: 2
- Open Issues: 3
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Tweetalyzer
Tweetalyzer is a simple Python app to Stream and Visualise Tweets on specific topics.
Visualisation - Work In Progress.
### Tech
Tweetalyzer uses a number of open source projects to work properly:
* [Flask] - microframework for Python based on Werkzeug, Jinja 2.
* [Pandas] - pandas is an open source, library providing high-performance, easy-to-use data structures and data analysis tools for the Python.
* [Redis] - Redis is an open source, in-memory data structure store, used as a database, cache and message broker.
* [Celery] - Celery is an asynchronous task queue/job queue based on distributed message passing.
* [TextBlob] - TextBlob is a Python (2 and 3) library for processing textual data.
* [Tweepy] - An easy-to-use Python library for accessing the Twitter API.
### Installation
You need Python 3.*, its dependency packages, and the above mentioned packages installed globally:
```sh
$ git clone https://github.com/Dineshkarthik/tweetalyzer.git
$ cd tweetalyzer
$ pip install -r requirements.txt
```
### Execution
```sh
$ python tweetalyzer.py
$ python server.py
$ celery -A tweetprocessor worker --loglevel=info
```
* Running `tweetalyzer.py` will start streaming the tweets in the specified topics and will store them in text files.
* Running `sever.py` will start the flask server and the visualisation can be accessed at `http://localhost:5000`
* server.py will start the flask server in port 5000 by default which can be started in any other port like `python server.py -p 8000`
* Command `celery -A tweetprocessor worker --loglevel=info` will start the celery workers which will start processing the stored tweets, which happens every one minute.
## Configuration
access_token: 'your_access_token'
access_token_secret: 'your_access_token_secret'
consumer_key: 'your_consumer_key'
consumer_secret: 'your_consumer_secret'
topics: ['python', 'golang', '#ruby']
* To get the above tokens for authenticating the python application to access your twitter account follow the Steps from 2 to 4 in [here](https://www.digitalocean.com/community/tutorials/how-to-authenticate-a-python-application-with-twitter-using-tweepy-on-ubuntu-14-04).
* topics - add the list of topics that need to be streamed.
License
----
MIT
**Free Software, Hell Yeah!**
[D3.js]:
[Flask]:
[Pandas]:
[Redis]:
[Celery]:
[Twitter Bootstrap]:
[jQuery]:
[TextBlob]:
[Tweepy]: