Sentiment analysis on nltk movie reviews data set achieving more than 93% accuracy

---

Introduction

The main idea behind this project was to determine the sentiment (positive or negative) expressed by a movie review. In this project, I explored different types of feature models like the Top-N feature model and the bag-of-words model and brought them together.

---

Requirements

• Python (> 3.0)
• Pillow (comes with python by default)
• NLTK

---

Description

Training files along with pretrained models of the following models are added:

• Top-N feature model (accuracy: 0.807)
• Unigram model (accuracy: 0.703)
• Bigram model (accuracy: 0.786)
• Ngram model (accuracy: 0.912)

Results of all the models are added.

---

References

1. http://blog.chapagain.com.np/python-nltk-sentiment-analysis-on-movie-reviews-natural-language-processing-nlp/
2. https://ataspinar.wordpress.com/2016/02/01/sentiment-analysis-with-bag-of-words-part-2/
3. https://www.nltk.org/book/