Proprosed and experimented a text-based movie score prediction system.
- Chengbo Zang (cz2678)
- Shengbo Chen (sc4918)
- Chenhao Wang (cw3355)
Our project aims to collect website reviews related to different movies, extract meaningful information and predict the actual votes. This will be helpful for the audiences to choose movies that are worth watching, as well as helping the cinemas to make more reasonable arrangements on movie schedules.
A few of the key tools and environments are listed as follows.
- python=3.8.8
- ipykernel=5.3.4
- pyspark==3.1.2
- nltk=3.6.1
- numpy==1.19.5
- pandas=1.2.4
- matplotlib=3.3.4
- tmdbv3api==1.7.6
- IMDbPY==2021.4.18
- tweepy==3.9.0
For detailed evironments, see requirements.txt.
- For final visualization,
#codes to run for visulization
- To see development interface, open and execute blocks in development.ipynb.
.
├── README.md
├── config.py
├── data
│ ├── movies.csv
│ ├── movies_pp.csv
│ ├── predicts.csv
│ └── topics.txt
├── development.ipynb
├── environment.yml
└── utils
├── analysis.py
├── context.py
├── models.py
├── movies.py
├── preprocess.py
├── sparkStreaming.py
├── twitter.py
└── twitterHTTPClient.py
2 directories, 16 files
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