Text classification implementation with TensorFlow can be simple. One of the areas where text classification can be applied -- chatbot text processing and intent resolution. I will describe step by step in this post, how to build TensorFlow model for text classification and how classification is done. Please refer to my previous post related to similar topic -- Contextual Chatbot with TensorFlow, Node.js and Oracle JET -- Steps How to Install and Get It Working. I would recommend to go through this great post about chatbot implementation -- Contextual Chatbots with Tensorflow.
Includes implementation of TreeLSTMs as described in "Improved Semantic Representations From Tree-Structured Long Short-Term Memory Networks" by Kai Sheng Tai, Richard Socher, and Christopher D. Manning. Also includes implementation of TreeGRUs derived using similar methods. Code for evaluation on the Standford Sentiment Treebank (used by the paper) is also available in sentiment.py. To run this, you'll need to download the relevant data. After this, you'll also need to prepare the Glove vectors as .npy