- Applied principles of NLP(natural language processing) and data science together to draw insights such as - most frequent words distribution, average chapter length, most frequent phrases(bi, tri and quad-grams), names of characters, places and events, sentiment analysis on sentence and paragraph level and chapter-wise text summarization.
- reporting findings through word-clouds, bar plots and histograms.
- Pandas dataframes containing information about phrase distributions and sentiment analysis.
NLP, exploratory data analysis, sentiment analysis, NER, text summarization.