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Tutorial 2B: Natural Language Processing (NLP) - Sentiment analysis

Introduction​

Overview​

  • Sentiment analysis, also known as opinion mining, is a subfield of Natural Language Processing (NLP) that tries to identify and extract opinions from a given text. Sentiment analysis aims to gauge the attitudes, sentiments, and emotions of a speaker or writer based on the computational treatment of subjectivity in a text. This can be in the form of like or dislike binary rating or in the form of numerical ratings from 1 to 5.
  • Sentiment Analysis is an important sub-field of NLP. It can help to create targeted brand messages and assist a company in understanding consumer’s preferences. These insights could be critical for a company to increase its reach and influence across a range of sectors.
  • Here are some of the uses of Sentiment Analysis from a business perspective:
    • Provide audience insight
    • Support customer service
    • Augment good PR practices
    • Drive proactive business solutions
    • Measure the ROI of the marketing campaign
  • This tutorial describes how to launch a sentiment analysis experiment, including the experiment settings and custom recipe, and concludes with a challenge to test your skills.

Objectives​

In this tutorial, we will focus on the following objectives:

  • Learn to build and fine-tune an NLP model that can effectively classify sentiment in fine food reviews from Amazon. In other words, conduct a Sensitivity Analysis on the various customer reviews.
  • Launch a Sentiment Analysis Experiment using the specified settings and custom recipe within the H2O Driverless AI environment.
  • Complete the challenge to test your understanding and application of sentiment analysis in a practical setting.
note

It is highly recommended that you go over the entire tutorial before starting the experiment.

Prerequisites​

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