Metadata-Version: 2.1
Name: basictwitternlp
Version: 0.1.0
Summary: Scrape Twitter based off query and runs NLTK vader and cos similarity model
Home-page: 
Author: Robert Edwardes
Author-email: robie@fairlines.org
License: UNKNOWN
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3 :: Only
Requires-Python: >=3.6, <4
Description-Content-Type: text/markdown
License-File: LICENSE.txt

Basic Twitter NLP
----
Description: Simple set of comands to work with twitter and text setiment analysis

NEEDED: Twitter Developer account for bearer token [Twitter Developer]('https://developer.twitter.com/en/docs/platform-overview')

### Getting Started 
-----
Build Database to Store tweets and analysis. It will create the 5 tables needed for the process 
```python
db_init(con)
```
Add Querys. It takes any query that is allowed by your Twitter api acesses. [Twitter Query Help Guide]('https://developer.twitter.com/en/docs/tutorials/building-high-quality-filters')
```python
add_query(twitter_query, con)
```
Run Process. This will run the scrape and NLP process and store them in the tables
```python
run_tw_nlp(con, client, query=None, inc_rt=False)
````
con : SQLite3 connection   ``` con = sqlite3.connection('DATABASE_NAME.db')```

client : twitter bearer token ``` client =  tweepy.Client(bearer_token='bearer') ```

query : query index number which can be found in the "query_idx" column in the queries table

inc_rt : to include retweets in text analysis

