Text data must be encoded as numbers to be used as input or output for machine learning and deep learning models. The Keras deep learning library provides some basic tools to help you prepare your text data. In this tutorial, you will discover how you can use Keras to prepare your text data. How to Prepare Text Data for Deep Learning with Keras Photo by ActiveSteve, some rights reserved. A good first step when working with text is to split it into words.
This article is a comprehensive overview of Topic Modeling and its associated techniques. In natural language understanding (NLU) tasks, there is a hierarchy of lenses through which we can extract meaning -- from words to sentences to paragraphs to documents. At the document level, one of the most useful ways to understand text is by analyzing its topics. The process of learning, recognizing, and extracting these topics across a collection of documents is called topic modeling. In this post, we will explore topic modeling through 4 of the most popular techniques today: LSA, pLSA, LDA, and the newer, deep learning-based lda2vec.
Latent Dirichlet Allocation (LDA) is a classical way to do a topic modelling. Topic modeling is a unsupervised learning and the goal is group different document to same "topic". Typical example is clustering a news to corresponding category including "Finance", "Travel", "Sport" etc. Before word embeddings we may use Bag-of-Words in most of the time. However, the world changed after Mikolov et al. introduce word2vec (one of the example of Word Embeddings) in 2013.
Topic modeling is an unsupervised learning approach to document clustering based on the topics of their content. In this article, we will create a model using a topic modeling technique called Non-Negative Matrix Factorization (NMF) to infer the main themes existing in a dataset of hotel reviews, analyze how accurate this classification is across all documents, and predict the topic of a new document with our trained model. In this domain, a topic refers to a collection of terms that are frequently used in combination with documents of the same theme. Therefore, topic modeling key outputs are: a list of topics and the list of documents that are correlated with each topic. For this article, we will be using this dataset available in Kaggle that contains 515k customer reviews in English rating their experience in hotels across Europe.