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Mobile Artificial Intelligence Projects

You're reading from  Mobile Artificial Intelligence Projects

Product type Book
Published in Mar 2019
Publisher Packt
ISBN-13 9781789344073
Pages 312 pages
Edition 1st Edition
Languages
Authors (3):
Karthikeyan NG Karthikeyan NG
Profile icon Karthikeyan NG
Arun Padmanabhan Arun Padmanabhan
Profile icon Arun Padmanabhan
Matt Cole Matt Cole
Profile icon Matt Cole
View More author details

Table of Contents (12) Chapters

Preface 1. Artificial Intelligence Concepts and Fundamentals 2. Creating a Real-Estate Price Prediction Mobile App 3. Implementing Deep Net Architectures to Recognize Handwritten Digits 4. Building a Machine Vision Mobile App to Classify Flower Species 5. Building an ML Model to Predict Car Damage Using TensorFlow 6. PyTorch Experiments on NLP and RNN 7. TensorFlow on Mobile with Speech-to-Text with the WaveNet Model 8. Implementing GANs to Recognize Handwritten Digits 9. Sentiment Analysis over Text Using LinearSVC 10. What is Next? 11. Other Books You May Enjoy

Sentiment Analysis over Text Using LinearSVC

In this chapter, we are going to build an iOS application to do sentiment analysis over text and image through user input. We will use existing data models that were built for the same purpose by using LinearSVC, and convert those models into core machine learning (ML) models for ease of use in our application.

Sentiment analysis is the process of identifying a feeling or opinion that is inspired by any given data in the form of text, image, audio, or video. There are a lot of use cases for sentiment analysis. Even now, political parties can easily identify the general mindset of the people who are going to elect them and they also have the potential to change that mindset.

Let's take a look at building our own ML model on sentiment analysis from an existing dataset. In this chapter, we will look at the following topics:

  • Building...
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