chapter nine
9 Natural Language Processing with TensorFlow: Sentiment Analysis
This chapter covers,
- Understanding the basic characteristics of a classification based text dataset and cleaning the text inputs with a combination of python libraries like Pandas and NLTK
- Analysing text specific attributes such as the vocabulary size and sequence length and converting text to numerical representations to feed into the model
- Creating data pipeline to handle text sequences with TensorFlow
- Implementing a recurrent deep learning model for analysing sentiments in reviews and understand the underlying mechanics of deep sequential models like LSTMs in the process
- Training the model on an imbalanced product reviews (different amounts of examples for each label)
- Recognizing the role of word embeddings in NLP and implementing word embeddings to improve the deep learning model performance