Decisive Polarity Determination within Social Networks (eBook, ePUB)
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Olivia Parker's "Decisive Polarity Determination within Social Networks" is a complete guide to mood analysis and how to use it to find the definitive polarity in social networks. The book gives an outline of sentiment analysis, a way to figure out how someone feels about something written down, and how it can be used to label social media posts as positive, negative, or neutral.The writer looks at a number of methods and tools used for mood analysis, such as text mining, machine learning algorithms, and natural language processing. Vallikannu also gives case studies and examples of how mood a...
Olivia Parker's "Decisive Polarity Determination within Social Networks" is a complete guide to mood analysis and how to use it to find the definitive polarity in social networks. The book gives an outline of sentiment analysis, a way to figure out how someone feels about something written down, and how it can be used to label social media posts as positive, negative, or neutral.
The writer looks at a number of methods and tools used for mood analysis, such as text mining, machine learning algorithms, and natural language processing. Vallikannu also gives case studies and examples of how mood analysis can be used on social networks like Twitter and Facebook to find the clearest polarity of posts about certain brands, events, or topics.
People who work in marketing, social media analysis, or data science and want to learn how to use sentiment analysis to find the most extreme opinions on social networks should read this book. Other people who might find it useful are researchers and professors who want to know about the newest methods and trends in sentiment analysis and social media analysis.
Overall, "Decisive Polarity Determination within Social Networks" is a thorough and insightful look at sentiment analysis and how it can be used in social media research. Anyone who wants to keep up with the latest research and trends in this area that changes so quickly should read it.
The writer looks at a number of methods and tools used for mood analysis, such as text mining, machine learning algorithms, and natural language processing. Vallikannu also gives case studies and examples of how mood analysis can be used on social networks like Twitter and Facebook to find the clearest polarity of posts about certain brands, events, or topics.
People who work in marketing, social media analysis, or data science and want to learn how to use sentiment analysis to find the most extreme opinions on social networks should read this book. Other people who might find it useful are researchers and professors who want to know about the newest methods and trends in sentiment analysis and social media analysis.
Overall, "Decisive Polarity Determination within Social Networks" is a thorough and insightful look at sentiment analysis and how it can be used in social media research. Anyone who wants to keep up with the latest research and trends in this area that changes so quickly should read it.
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