Summary

In this chapter, we looked at how to build a sentiment analysis model that gives us state-of-the-art results. We used an IMDb dataset that had positive and negative movie reviews and understood the dataset. We applied the machine learning algorithm in order to get the baseline model. After that, in order to optimize the baseline model, we changed the algorithm and applied deep-learning-based algorithms. We used glove, RNN, and LSTM techniques to achieve the best results. We learned how to build sentiment analysis applications using Deep Learning. We used TensorBoard to monitor our model's training progress. We also touched upon modern machine learning algorithms as well as Deep Learning techniques for developing sentiment analysis, and the Deep Learning approach works best here.

We used a GPU to train the neural network, so if you discover that it needs more computation power from your end to train the model, then you can use the Google cloud or Amazon Web Services (AWS) GPU-based...