It analyses the movie review entered by a user for any specific movie and analyses what is the sentiment of the review. It helps the companies rate the movie and understand crowd sentiment regarding it. Sentiment analysis is a natural language processing problem where text is understood and the underlying intent is predicted. Sentiment analysis, also called opinion mining, is the field of study that analyzes people’s opinions, sentiments, evaluations, appraisals, attitudes, and emotions towards entities such as products, services, organizations, individuals, issues, events, topics, and their attributes
Here's why:
- Sentiment analysis has long been a problem for business, marketing and management areas for more value earned in the decision process
- Sentiment Analysis also helps organisations measure the ROI of their marketing campaigns and improve their customer service.
- Since sentiment analysis gives the organisations a sneak peek into their customer’s emotions, they can be aware of any crisis that’s to come well in time – and manage it accordingly.
The dataset is the arge Movie Review Dataset often referred to as the IMDB dataset.
The Large Movie Review Dataset (often referred to as the IMDB dataset) contains 25,000 highly polar moving reviews (good or bad) for training and the same amount again for testing. The problem is to determine whether a given moving review has a positive or negative sentiment.
The data was collected by Stanford researchers and was used in a 2011 paper PDF where a split of 50/50 of the data was used for training and test.
This was build using following frameworks, libraries and softwares.
To run this project you need to follow the following steps.
To run this project you need to follow the following steps.
These are the prerequisites you need to build this bot as well as run it.
cmd:\ pip install tensorflow
cmd:\ pip install keras- Create conda environment and create project in this environment
- After installing requirements in above Modules LIST
- You need python idle such as Jupyter notebook or spyder
Sentiment analysis is like having a private detective listening to what your customers are saying—everywhere.
Sure, your customers might give some feedback to your customer service team directly. But they are also going to give their honest opinion on other platforms such as Facebook, discussion forums, Amazon, Twitter… the list really is endless. Advanced sentiment analysis can not only uncover what customers are saying, but why they are saying it. Here are the key areas for how sentiment analysis help businesses.
- Brand Monitoring
- Improving Your Customer Support
- Tracking Your Employees’ Feedback
- Providing Better Product Analytics
- Monitoring Market Research
- Keeping an Eye on Your Competition
- Tracking User Generated Content
- Uncovering Brand Influencers
- Social Media Monitoring
- Managing a Crisis Better
For more examples, please refer to the Article
See the open issues for a list of proposed features (and known issues).
Contributions are what make the open source community such an amazing place to be learn, inspire, and create. Any contributions you make are greatly appreciated.
- Fork the Project
- Create your Feature Branch (
git checkout -b feature/AmazingFeature) - Commit your Changes (
git commit -m 'Add some AmazingFeature') - Push to the Branch (
git push origin feature/AmazingFeature) - Open a Pull Request
- MIT license
- Copyright 2020 © Aditya Mangla.
Aditya Mangla - @aadimangla - aadimangla@gmail.com - adityamangla.com
Project Link: https://github.com/aadimangla/IMDB-Movie-Reviews-Sentiment-Analysis