Sentiment analysis or opinion mining is a natural language processing (NLP) technique used to determine the polarity of a given text - whether it is positive, negative, or neutral. One of the main issues with sentiment analysis is the extraction of emotions from opinion reviews and the identification of fake positive and fake negative reviews from these opinion reviews.
Sentiment analysis is done to extract people's opinions from a large number of unfiltered texts by classifying them into sentiment classes namely positive, negative or neutral. It is used in various fields like product analysis, market research, etc.
The objective of our project is to study various sentiment detection algorithms to analyse movie reviews and differentiate genuine reviews from fake ones and improve on them to overcome their limitations in various fields and make the system more usable.
The IMDB dataset has 50K movie reviews for natural language processing or Text analytics. This is a dataset for binary sentiment classification containing substantially more data than previous benchmark datasets. A set of 25,000 highly polar movie reviews are available for training and 25,000 for testing.
The raw dataset can be found using the following link: http://ai.stanford.edu/~amaas/data/sentiment/
- Open VS Code and open the directory where you wish to have this project
- Open the terminal and type the following command
git clone https://github.com/NotHari/Fake-Reviews-Detection-on-Movie-Reviews-through-Sentiment-Analysis.git - This command will clone the entire project in your local system
- Now open
code.ipynband change the path of the dataset if needed - Finally, click on the
Run Alloption to run all the cells and view results