Sentiment Analysis on Movies Reviews

You can use Docker to easily install all the needed packages and libraries:

$ docker build -t nlp_project -f Dockerfile .

$ docker run --rm --net host -it \
    -v $(pwd):/home/app/src \
    nlp_project \
    bash

It doesn't matter if you are inside or outside a Docker container, in order to execute the project you need to launch a Jupyter notebook server running:

$ jupyter notebook

Then, inside the file Sentiment_Analysis_NLP.ipynb, you can see the project statement, description and also which parts of the code you must complete in order to solve it.

We've added some basic tests to Sentiment_Analysis_NLP.ipynb that you must be able to run without errors in order to approve the project. If you encounter some issues in the path, make sure to be following these requirements in your code:

• Every time you need to run a tokenizer on your sentences, use nltk.tokenize.toktok.ToktokTokenizer.
• When removing stopwords, always use nltk.corpus.stopwords.words('english').
• For Stemming, use nltk.porter.PorterStemmer.
• For Lematizer, use Spacy pre-trained model en_core_web_sm.

You can use others methods if you want to do extra experimentation but do it outside the code used to run the tests. Otherwise, they may fail for some specific cases.