Tutorial on Sentimental Analysis using Pytorch for Beginners
Sequential problems are widely used in machine learning for many applications like chatbot creation, language translation, text generation, and text classification.
Pytorch is one of the popular deep learning libraries to make a deep learning model.
In this tutorial, we are going to work on a review classification problem. We will classify the movie review into two classes: Positive and Negative. It will be a code walkthrough with all the steps needed for the simplest sentimental analysis problem. This is for someone who just wants to get started with NLP.
For training the deep learning model using sequential data, we have to follow two common steps:
- Preprocess the Sequence data to remove un-nessasory words
- Convert text data into the tensor or array format
Step1: Get the dataset and make the list of reviews and labels,
Step2: We need to remove all the punctuation like ‘ !”#$%&\’()*+,-./:;<=>?@[\\]^_`{|}~ ’ because it is not very important to us while interpreting text using a deep learning model
Step3: Remove Punctuation and get all the words from review dataset
Step4: Count all the words and sort it based on counts
Step5: Create a dictionary to convert words to Integers based on the number of occurrence of the word
Step6: Encode review in to list of Integer by using above dictionary
Step7: make all the encoded_review of the same length
Step8: Our dataset has ‘Positive’ and ‘Negative’ as a label, it will be easy if we have 1 and 0, instead of ‘Positive’ and ‘Negative’
Step9: Split this feature data into Training and Validation set
Step10: Analyze the dataloader data
Step11: Create DataLoader objects for Pytorch model
Step12: Create an LSTM, RNN or any other model Architecture and test it to get better accuracy
Step13: Initialize the model
Step14: Train the model
Step15: Test the model accuracy
Here is the link of my Kaggle kernel,
Follow my telegram channel to get awesome blogs, projects, and learning opportunity for Python, Machine Learning and Data Science Stuff.
Stay Pythonic.
