Sentiment Analysis with Recurrent Neural Networks in TensorFlow from Pluralsight

Recurrent neural networks (RNNs) are ideal for considering sequences of data. In this course, you'll explore how word embeddings are used for sentiment analysis using neural networks.

Sentiment analysis and natural language processing are common problems to solve using machine learning techniques. Having accurate and good answers to questions without trudging through reviews requires the application of deep learning techniques such as neural networks. In this course, Sentiment Analysis with Recurrent Neural Networks in TensorFlow, you'll learn how to utilize recurrent neural networks (RNNs) to classify movie reviews based on sentiment. First, you'll discover how to generate word embeddings using the skip-gram method in the word2vec model, and see how this neural network can be optimized by using a special loss function, the noise contrastive estimator. Next, you'll delve into understanding RNNs and how to implement an RNN to classify movie reviews, and compare and contrast the neural network implementation with a standard machine learning model, the Naive Bayes algorithm. Finally, you'll learn how to implement the same RNN but with pre-built word embeddings. By the end of this course, you'll be able to understand and implement word embedding algorithms to generate numeric representations of text, and know how to build a basic classification model with RNNs using these word embeddings.

What's inside

Syllabus

Course Overview

Applying Word Vector Embeddings to Language Modeling

Implementing Word Embeddings in TensorFlow

Performing Sequence Classification with RNNs

Implementing Sequence Classification Using RNNs in TensorFlow

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Suitable for students with no experience in natural language processing or those with some knowledge who wish to reinforce the concepts

Taught by Janani Ravi, a notable instructor in this domain

Implements word embeddings generated from the word2vec model

Employs the skip-gram method to create word embeddings

Compares an RNN implementation to Naive Bayes for sentiment analysis, providing a versatile perspective

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Sentiment Analysis with Recurrent Neural Networks in TensorFlow with these activities:

Review word embedding concepts

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Review the fundamental concepts of word embeddings and their applications

Browse courses on Word Embeddings

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Revisit the basics of word embeddings, including Skip-Gram and CBOW
Explore different word embedding models, such as GloVe and ELMo

Review core concepts of machine learning

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Revisit essential concepts in machine learning, including dimensionality reduction, feature engineering, natural language processing, and hidden Markov models to strengthen your foundation before diving into the course.

Browse courses on Hidden Markov Models

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Review lecture notes or textbooks on machine learning fundamentals
Complete practice problems on essential concepts

Follow a tutorial on implementing Word2Vec

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Get hands-on practice implementing and training a Word2Vec model

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Find a tutorial on implementing Word2Vec in TensorFlow
Follow the tutorial step-by-step and implement the model
Train the model on a dataset of your choice

Six other activities

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Complete tutorials on word embeddings and RNNs

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Explore online tutorials and resources to gain practical experience with word embeddings and recurrent neural networks. Implement the skip-gram model and build a basic RNN for sequence classification.

Browse courses on Word2Vec

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Search for tutorials on word embeddings and RNNs
Follow the steps and complete the exercises in the tutorials
Experiment with different hyperparameters and datasets

Complete coding exercises on RNNs

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Reinforce your understanding of RNNs through practical coding exercises

Browse courses on Recurrent Neural Networks

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Find online coding exercises or platforms that offer RNN-related problems
Attempt to solve the exercises and implement RNNs to solve the problems
Review your solutions and identify areas for improvement

Develop a sentiment analysis model

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Build a complete end-to-end sentiment analysis model using the techniques learned in the course. Gather a dataset of movie reviews, preprocess the text, and train a recurrent neural network to classify the sentiment of movie reviews.

Browse courses on Sentiment Analysis

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Gather a dataset of movie reviews
Preprocess the text data by removing stop words and stemming
Generate word embeddings using the skip-gram model
Implement a recurrent neural network for sequence classification
Evaluate the model's performance on a test set

Create a short blog post or article about RNNs

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Demonstrate your understanding of RNNs by explaining them in your own words

Browse courses on Recurrent Neural Networks

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Research RNNs and their different types, such as LSTM and GRU
Write a blog post or article explaining the concepts and applications of RNNs
Share your post or article with others for feedback

Develop a sentiment analysis model using RNNs

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Apply your knowledge of RNNs to build a practical sentiment analysis model

Browse courses on Sentiment Analysis

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Gather a dataset of labeled movie reviews
Preprocess the data and generate word embeddings
Build an RNN model for sentiment classification
Train and evaluate the model

Mentor a junior or beginner in the field of NLP

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Share your knowledge and support the growth of others in the field

Browse courses on Natural Language Processing

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Identify a junior or beginner who is interested in NLP
Share your knowledge and experience in RNNs and sentiment analysis
Provide guidance and support as they learn and develop their skills

Career center

Learners who complete Sentiment Analysis with Recurrent Neural Networks in TensorFlow will develop knowledge and skills that may be useful to these careers:

Data Scientist

Data Scientists help companies to gather, analyze, and interpret data in order to make informed decisions. In this role, you would use the sentiment analysis techniques and neural networks you learn about in this course to help your company understand customer feedback, predict trends, and make better decisions about product development and marketing.

See salaries and explore the career path for Data Scientist

Machine Learning Engineer

Machine Learning Engineers build and maintain machine learning models that can be used to automate tasks, improve decision-making, and make predictions. In this role, you would use the sentiment analysis techniques and neural networks you learn about in this course to build models that can help companies to automate customer service, detect fraud, and improve their products and services.

See salaries and explore the career path for Machine Learning Engineer

NLP Engineer

NLP Engineers build and maintain natural language processing models that can be used to automate tasks, improve decision-making, and make predictions. In this role, you would use the sentiment analysis techniques and neural networks you learn about in this course to build models that can help companies to automate customer service, detect fraud, and improve their products and services.

See salaries and explore the career path for NLP Engineer

Software Engineer

Software Engineers design, develop, and maintain software applications. In this role, you would use the sentiment analysis techniques and neural networks you learn about in this course to build software applications that can help companies to analyze customer feedback, predict trends, and make better decisions about product development and marketing.

See salaries and explore the career path for Software Engineer

Data Analyst

Data Analysts collect, analyze, and interpret data in order to help businesses make better decisions. In this role, you would use the sentiment analysis techniques and neural networks you learn about in this course to help your company understand customer feedback, predict trends, and make better decisions about product development and marketing.

See salaries and explore the career path for Data Analyst

Computational Linguist

Computational Linguists study the relationship between language and computation. In this role, you would use the sentiment analysis techniques and neural networks you learn about in this course to develop new methods for processing and understanding natural language.

See salaries and explore the career path for Computational Linguist

Statistician

Statisticians collect, analyze, and interpret data in order to help businesses make better decisions. In this role, you would use the sentiment analysis techniques and neural networks you learn about in this course to help your company understand customer feedback, predict trends, and make better decisions about product development and marketing.

See salaries and explore the career path for Statistician

Risk Analyst

Risk Analysts identify and assess risks that could affect a company's financial performance. In this role, you would use the sentiment analysis techniques and neural networks you learn about in this course to develop models that can help companies to identify and mitigate risks.

See salaries and explore the career path for Risk Analyst

Quantitative Analyst

Quantitative Analysts use mathematical and statistical techniques to analyze financial data. In this role, you would use the sentiment analysis techniques and neural networks you learn about in this course to develop models that can help investors to make better decisions about which stocks to buy and sell.

See salaries and explore the career path for Quantitative Analyst

Operations Research Analyst

Operations Research Analysts use mathematical and statistical techniques to solve problems in a variety of industries. In this role, you would use the sentiment analysis techniques and neural networks you learn about in this course to develop models that can help companies to improve their operations and make better decisions.

See salaries and explore the career path for Operations Research Analyst

Financial Analyst

Financial Analysts analyze financial data in order to help companies make better decisions about investments and other financial matters. In this role, you would use the sentiment analysis techniques and neural networks you learn about in this course to develop models that can help companies to make better decisions about which stocks to buy and sell.

See salaries and explore the career path for Financial Analyst

Market Research Analyst

Market Research Analysts collect and analyze data about customers and markets. In this role, you would use the sentiment analysis techniques and neural networks you learn about in this course to develop models that can help companies to understand customer feedback, predict trends, and make better decisions about product development and marketing.

See salaries and explore the career path for Market Research Analyst

Editor

Editors review and edit written material for clarity, accuracy, and style. In this role, you would use the sentiment analysis techniques and neural networks you learn about in this course to develop tools and techniques for improving the quality of written material.

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Technical Writer

Technical Writers create documentation for software, hardware, and other technical products. In this role, you would use the sentiment analysis techniques and neural networks you learn about in this course to develop documentation that is clear, concise, and easy to understand.

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Librarian

Librarians help people to find and use information. In this role, you would use the sentiment analysis techniques and neural networks you learn about in this course to develop new ways to organize and search for information.

See salaries and explore the career path for Librarian