Submit a project which uses one of the methodologies.
Example of projects proposals are:
This project aims to classify movie reviews using sentiment analysis. The dataset I am going to use is from Kaggle and can be found here: https://www.kaggle.com/c/sentiment-analysis-on-movie-reviews/data
This project predicts fake news. To do so, I use data on article titles and a Naive Bayes classifier. The dataset I am going to use is from Kaggle and can be downloaded here:
For the final project, you are expected to submit a .ipynb file that replicates your analysis and contains a description of your data, text-preprocessing, algorithm, and findings. Please make sure to discuss your text preprocessing (why are you doing what you are doing) and findings.
Checklist to have in mind
Describe your data. What are the variables you are working with? Are there missing values? Are there wrongly encoded data? Describe how you approached fixing these issues (if applicable).
in your specific case, does it make sense to remove numeric characters, punctuation, stopwords, lemmatize, or stem the tokens? Would it make more sense to use single tokens, bigrams or n-ngrams? Argue why you are making certain choices rather than others.
What are the features of the algorithm that you are using?
Describe your findings. What do they say? How to interpret the results? How would (how do) your results change if you improve pre-processing?
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