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Sentiment analysis (also known as opinion mining or emotion AI) is the use of natural language processing, text analysis, computational linguistics, and biometrics to systematically identify, extract, quantify, and study affective states and subjective information.
Hutto, Clayton J., and Eric Gilbert. "Vader: A parsimonious rule-based model for sentiment analysis of social media text." Eighth international AAAI conference on weblogs and social media. 2014. Gilbert, Eric, and Karrie Karahalios. "Predicting tie strength with social media."
In other words, data analysis is the phase that takes filtered data as input and transforms that into information of value to the analysts. Many different types of analysis can be performed with social media data, including analysis of posts, sentiment, sentiment drivers, geography, demographics, etc. The data analysis step begins once we know ...
These forces are then measured via statistical analysis of the nodes and connections between these nodes. [8] Social analytics also uses sentiment analysis, because social media users often relay positive or negative sentiment in their posts. [11] This provides important social information about users' emotions on specific topics. [12] [13] [14]
Johan Lambert Trudo Maria Bollen (born 1971) is a scientist investigating complex systems and networks, the relation between social media and a variety of socio-economic phenomena such as the financial markets, public health, and social well-being, as well as Science of Science with a focus on impact metrics derived from usage data.
Schumaker also works in the field of Sports Analytics authoring numerous papers on greyhound [7] and harness racing prediction [8] as well as using Twitter sentiment to predict Premier League [9] and NFL matches. [10] He has also authored a book on the subject, Sports Data Mining (2010; ISBN 978-1-4419-6729-9).
In this podcast, Motley Fool host Dylan Lewis and analysts Ron Gross and Jason Moser discuss: What the December Fed minutes, latest jobs numbers, and final holiday shopping figures tell us about ...
2016: Technical Analysis, on the use of granular data for the construction of popular indicators such as the put/call ratio. [5] 2014: Investing With The Trend, on recognizing the importance of market regimes in the application of sentiment data. [6] 2014: Financial Tracker, on the potential for Twitter as a source of social sentiment. [7]