Book Image

Natural Language Processing with TensorFlow

By : Motaz Saad, Thushan Ganegedara
Book Image

Natural Language Processing with TensorFlow

By: Motaz Saad, Thushan Ganegedara

Overview of this book

Natural language processing (NLP) supplies the majority of data available to deep learning applications, while TensorFlow is the most important deep learning framework currently available. Natural Language Processing with TensorFlow brings TensorFlow and NLP together to give you invaluable tools to work with the immense volume of unstructured data in today’s data streams, and apply these tools to specific NLP tasks. Thushan Ganegedara starts by giving you a grounding in NLP and TensorFlow basics. You'll then learn how to use Word2vec, including advanced extensions, to create word embeddings that turn sequences of words into vectors accessible to deep learning algorithms. Chapters on classical deep learning algorithms, like convolutional neural networks (CNN) and recurrent neural networks (RNN), demonstrate important NLP tasks as sentence classification and language generation. You will learn how to apply high-performance RNN models, like long short-term memory (LSTM) cells, to NLP tasks. You will also explore neural machine translation and implement a neural machine translator. After reading this book, you will gain an understanding of NLP and you'll have the skills to apply TensorFlow in deep learning NLP applications, and how to perform specific NLP tasks.
Table of Contents (16 chapters)
Natural Language Processing with TensorFlow
Contributors
Preface
Index

NLP for social media


Now we will discuss how NLP has influenced social media mining. Here we will discuss findings presented in several papers. These findings include detecting rumors from truth and detecting emotions and identifying manipulations of words by politicians, for example, to gain more support (that is, political framing).

Detecting rumors in social media

In Detect Rumors Using Time Series of Social Context Information on Microblogging Websites [20], Jing Ma and others propose a way to detect rumors in microblogs. Rumors are stories or statements that are either deliberately false or for which the truth is not verified. Identifying rumors in their early phases is important to prevent false/invalid information being delivered to people. In this paper, an event is defined as a set of microblogs relevant to that event. A time-sensitive context feature is derived for each microblog and they are binned into time intervals depending on the time the microblog appeared. Thereafter, they...