/IMDb-Sentiment-Analysis

Cardiff University

Primary LanguagePython

IMDb-Sentiment-Analysis

Large Movie Review Dataset v1.0

Overview

This dataset contains movie reviews along with their associated binary sentiment polarity labels. It is intended to serve as a benchmark for sentiment classification. This document outlines how the dataset was gathered, and how to use the files provided.

Dataset

The core dataset contains 25,000 reviews split into train, development and test sets. The overall distribution of labels is roughly balanced.

Files

Each folder contains files with negative (neg) and positive (reviews). One review per line.

Reference paper

@InProceedings{maas-EtAl:2011:ACL-HLT2011, author = {Maas, Andrew L. and Daly, Raymond E. and Pham, Peter T. and Huang, Dan and Ng, Andrew Y. and Potts, Christopher}, title = {Learning Word Vectors for Sentiment Analysis}, booktitle = {Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies}, month = {June}, year = {2011}, address = {Portland, Oregon, USA}, publisher = {Association for Computational Linguistics}, pages = {142--150}, url = { http://www.aclweb.org/anthology/P11-1015 }

Linux Terminal Instructions

IMDb.py script requires python 2.7+. After printing the results of its performance on the test set, the model will ask the user to provide a new movie review. It will detect if this new review is positive or negative by returning 1 or 0 respectively.

1. The following libraries must be installed locally:

  • pandas
  • TfidfVectorizer from sklearn.feature_extraction.text
  • LogisticRegression from sklearn.linear_model
  • classification_report from sklearn.metrics

2. Use cd command to move to the directory where the script is located.

3. Before running the script from terminal, make sure that all the data frames (train, development and test) are saved in the same directory as the file.

4. Type the python version installed in your system followed by the name of the script, including the extension .py (i.e. python3 IMDb.py).

5. Wait until the performance results are printed.

6. Write a new movie review and hit Enter.

7. The model will ouput 1 for a positive review or 0 for a negative one.