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What is Sentiment Analysis (Opinion Mining)

Chapter 7
Intelligent Analytics With Advanced Multi-Industry Applications
Uses natural language processing and machine learning to analyze and mining text to interpret, interpret, and classify emotions and subjective information (i.e., sentiment) from information sources. Sentiment analysis is widely applied to detect sentiments in social networks, news, websites, and other online conversations to estimate brand reputation, customer views, and different public opinions.
Published in Chapter:
Exploring Cryptocurrency Sentiments With Clustering Text Mining on Social Media
Jiwen Fang (The University of Hong Kong, Hong Kong), Dickson K. W. Chiu (The University of Hong Kong, Hong Kong), and Kevin K. W. Ho (University of Guam, Guam)
Copyright: © 2021 |Pages: 15
DOI: 10.4018/978-1-7998-4963-6.ch007
Abstract
Social media has become a popular communication platform and aggregated mass information for sentimental analysis. As cryptocurrency has become a hot topic worldwide in recent years, this chapter explores individuals' behavior in sharing Bitcoin information. First, Python was used for extracting around one month's set of Tweet data to obtain a dataset of 11,674 comments during a month of a substantial increase in Bitcoin price. The dataset was cleansed and analyzed by the process documents operator of RapidMiner. A word-cloud visualization for the Tweet dataset was generated. Next, the clustering operator of RapidMiner was used to analyze the similarity of words and the underlying meaning of the comments in different clusters. The clustering results show 85% positive comments on investment and 15% negative ones to Bitcoin-related tweets concerning security. The results represent the generally bullish environment of the cryptocurrency market and general user satisfaction during the period concerned.
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