To begin a sentiment mining project, we first need to understand how opinions are structured in text so we can find the best way to train the computer to deal with them. Opinion mining and sentiment analysis are considered sub-problems of the much larger field of natural language processing (NLP), and as such, are subject to many of the same unsolved issues in trying to account for all the quirks of human communication. However, sentiment mining is restricted in an important way, namely that its goal is not to understand the statements made by people, but rather to just figure out their tone. As we will see later, any one strategy for finding the sentiment of any given text may not be perfect, but this may not matter much if the amount of data is high and the stakes are comparatively low.
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You're reading from Mastering Data Mining with Python - Find patterns hidden in your data
Product typeBook
Published inAug 2016
Reading LevelIntermediate
Publisher
ISBN-139781785889950
Edition1st Edition
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Concepts
Author (1)
Megan Squire
Megan Squire
Megan Squire is a professor of computing sciences at Elon University. Her primary research interest is in collecting, cleaning, and analyzing data about how free and open source software is made. She is one of the leaders of the FLOSSmole.org, FLOSSdata.org, and FLOSSpapers.org projects.
Read more about Megan Squire
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Mastering Data Mining with Python - Find patterns hidden in your dataPublished in: Aug 2016Publisher: ISBN-13: 9781785889950
© 2016 Packt Publishing Limited All Rights Reserved
Author (1)
Megan Squire
Megan Squire is a professor of computing sciences at Elon University. Her primary research interest is in collecting, cleaning, and analyzing data about how free and open source software is made. She is one of the leaders of the FLOSSmole.org, FLOSSdata.org, and FLOSSpapers.org projects.
Read more about Megan Squire