How to Use Word Embedding Layers for Deep Learning with Keras - Machine Learning Mastery

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Word embeddings provide a dense representation of words and their relative meanings. They are an improvement over sparse representations used in simpler bag of word model representations. Word embeddings can be learned from text data and reused among projects. They can also be learned as part of fitting a neural network on text data. In this tutorial, you will discover how to use word embeddings for deep learning in Python with Keras.


Sentence Prediction Using a Word-level LSTM Text Generator -- Language Modeling Using RNN - WebSystemer.no

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This projected was originally was for one of my clients on up-work. You can find the code on my Github repo. Unfortunately, It does not contain the data-set(corpus) on which I've trained the model due to some privacy reasons. But you can train it on any text corpus which you want. Let's begin with the problem statement, so there is some XYZ company which deals with all sorts of repairing works related to electricity, plumbing everything that comes in a household.


How to Develop a Deep Learning Bag-of-Words Model for Predicting Movie Review Sentiment - Machine Learning Mastery

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Movie reviews can be classified as either favorable or not. The evaluation of movie review text is a classification problem often called sentiment analysis. A popular technique for developing sentiment analysis models is to use a bag-of-words model that transforms documents into vectors where each word in the document is assigned a score. In this tutorial, you will discover how you can develop a deep learning predictive model using the bag-of-words representation for movie review sentiment classification. How to Develop a Deep Learning Bag-of-Words Model for Predicting Sentiment in Movie Reviews Photo by jai Mansson, some rights reserved.


Sentiment Classification with Natural Language Processing on LSTM

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LSA itself is an unsupervised way of uncovering synonyms in a collection of documents.To start, we take a look how Latent Semantic Analysis is used in Natural Language Processing to analyze relationships between a set of documents and the terms that they contain. Then we go steps further to analyze and classify sentiment. We will review Chi Squared for feature selection along the way. We will use Recurrent Neural Networks, and in particular LSTMs, to perform sentiment analysis in Keras. Since, text is the most unstructured form of all the available data, various types of noise are present in it and the data is not readily analyzable without any pre-processing.