Sentiment analysis is contextual mining of text which identifies and extracts subjective information in source material, and helping a business to understand the social sentiment of their brand, product or service while monitoring online conversations.
In this repository sentiment analysis model is build on LSTM cells. LSTM cells are used because it stores spatial information of past inputs as compared to other networks .
Files and there uses are listed below
Data -> Folder used for data storage
Data Collection $prreprocessing -> Notebook for downloading data and preprocessing it.
Model -> Contains code for RNN Model
requirement -> txt file containg required lib
Python : 3.X
Pip
Steps for installation
- Download venv
pip install virtualenv - Create virtual environment
pip venv env - Activate virtual env
. env\bin\activate - Install required library
pip install -r requirement.txt
First, we'll pass in words to an embedding layer. We need an embedding layer because we have tens of thousands of words, so we'll need a more efficient representation for our input data than one-hot encoded vectors.
After input words are passed to an embedding layer, the new embeddings will be passed to LSTM cells. The LSTM cells will add recurrent connections to the network and give us the ability to include information about the sequence of words in the movie review data.
Finally, the LSTM outputs will go to a sigmoid output layer. We're using a sigmoid function because positive and negative = 1 and 0, respectively, and a sigmoid will output predicted, sentiment values between 0-1.
`Input(One Hot Encoded) -> Embedding Layer -> Lstm Cells --> Output`
The testing accuracy and loss are,
Test Loss : .326
Test Accuracy : .871
For contribution
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Clone Repo
git clone https://github.com/soni-ratnesh/Sentiment-Analysis-RNN.git -
Install Dependencies
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Verify your changes
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Submit Pull Request