Metadata-Version: 2.1
Name: mtsb
Version: 0.0.1
Summary: Python library that collects tweets about movies, performs a sentiment analysis and correlates it with the boxoffice result of the week after the movie release.
Home-page: https://github.com/federicodeservi/mtsb
Author: Federico De Servi, Alessandro Pontini
Author-email: federico@federicodeservi.com, a.pontini1@campus.unimib.it
License: MIT
Description: # MTSB
        
        MTSB (Movie Tweet Sentiment Boxoffice) is a python module that collects tweets about movies, performs a sentiment analysis and correlates it with the boxoffice result of the week after the movie release.
        
        ## Features
        
        * Collect tweets about movies
        * Creates hashtags for each movie
        * Performs sentiment analysis on those tweets using Google's API and returns a weighted geometric average of score and magnitude
        * Gets boxoffice data from boxofficemojo
        * Performs correlation between the sentiment analysis and boxoffice data
        
        ## Requirements
        
        * Python >= 3.5 (Might work on older version but it has not been tested)
        * All module dependencies are installed on installation, but you will also need:
            * You need to have set up correctly ntlk module: https://www.nltk.org/install.html
            * Performed at least once "ntlk.download()"
            * Already have API keys for tweet collection: https://developer.twitter.com/en.html
            * Already have API keys for Google Natural Language: https://cloud.google.com/natural-language/docs/setup
        * You also need to have the following services installed (tested on Linux system)
            * Jupyter-lab
            * MongoDB
            * Nifi
            * Kafka
            
        ## Installation
        
        In order to install MTSB you can simply:
        
        ```
        pip install mtsb
        ```
        
        ## Docs
        
        * tweet_collector()
        
        Collect tweets about movies. It lets you choose between movies released in 2019 and releasing in 2020. It then creates a list of hashtags based on the movie's name and top actors and uses it to collect tweets from twitter.
        
        ```
        import mtsb
        
        mtsb.tweet_collector()
        ```
        
        * sentiment()
        
        Performs sentiment analysis on collected tweets using Google's API and returns a weighted geometric average of score and magnitude.
        
        ```
        import mtsb
        mtsb.sentiment()
        ```
        
        * sentiment_boxoffice_all()
        
        Creates a dataframe with the following info for each movie:
            * Movie title
            * Weighted geometric average of score and magnitude (from sentiment() )
            * Gross boxoffice for the week after the movie release
        
        ```
        import mtsb
        
        mtsb.sentiment_boxoffice_all()
        ```
        
        * spearman_corr(df)
        
        Performs a spearman correlation using the df returned by sentiment_boxoffice_all().
        
        ```
        mtsb.spearman_corr(df)
        ```
        
        ## Acknowledgements
        
        Useful python libraries used:
        * [imdbpy library](https://github.com/alberanid/imdbpy/ "imdbpy library title")
        * [ntlk library](https://github.com/nltk/nltk "ntlk library title")
        * [beautifulSoup library](https://pypi.org/project/beautifulsoup4/ "beautifulSoup library title")
        
        ## Licence
        
        MIT licensed. See the bundled [LICENSE](https://github.com/federicodeservi/mtsb-analyzer/blob/master/LICENSE "LICENSE title") file for more details. 
        
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Natural Language :: English
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Topic :: Software Development :: Libraries
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Classifier: Intended Audience :: Developers
Requires-Python: >=3.5
Description-Content-Type: text/markdown
