[EXE] Intro to NLP using nltk and tf-idf for sentiment classification

[nikunj-taneja]

Learning Goals

• Word embeddings
• Stemming v/s Lemmatisation
• TF-IDF Algorithm
• Sentiment Classification

Exercise Statement

Use the nltk and scikit-learn libraries to learn basic NLP concepts like word embeddings, stemming, lemmatisation, tf-idf algorithm and apply these concepts in a sentiment classification task on the IMDb Movie Reviews dataset (http://ai.stanford.edu/~amaas/data/sentiment/)

Prerequisites

[Prerequisites, in terms of concepts or other exercises in this repo]

• Logistic Regression (basic understanding of binary classification)

Data source/summary:

[Provide a succinct summary of what the data is and where it is from]

• IMDB dataset having 50K movie reviews
• Source: http://ai.stanford.edu/~amaas/data/sentiment/

(Optional) Suggest/Propose Solutions

• Could build on the basic exercise to create more advanced ones or try to improve the results from tf-idf using neural networks.

[TITHI007]

Shall I work on these project as I have taken part in Hacktoberfest and I am want to make a project based on nlp..!? Pl. assign these issue to me .