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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.
Multimodal sentiment analysis is a technology for traditional text-based sentiment analysis, which includes modalities such as audio and visual data. [1] It can be bimodal, which includes different combinations of two modalities, or trimodal, which incorporates three modalities. [ 2 ]
Angoss – Angoss Text Analytics provides entity and theme extraction, topic categorization, sentiment analysis and document summarization capabilities via the embedded AUTINDEX – is a commercial text mining software package based on sophisticated linguistics by IAI (Institute for Applied Information Sciences), Saarbrücken.
Tweet data from 2009 including original text, time stamp, user and sentiment. Classified using distant supervision from presence of emoticon in tweet. 1,578,627 Tweets, comma, separated values Sentiment analysis 2009 [47] [48] A. Go et al. ASU Twitter Dataset Twitter network data, not actual tweets. Shows connections between a large number of ...
A buzz graph for the term "teszt" on Twitter in a social media monitoring tool. Social media analytics or social media monitoring is the process of gathering and analyzing data from social networks such as Facebook, Instagram, LinkedIn, or Twitter. A part of social media analytics is called social media monitoring or social listening. It is ...
Topsy Pro Analytics was a commercial web dashboard application that allowed users to conduct interactive analysis on keywords and authors by activity, influence, exposure, sentiment, language or geography. [19] Users could discover the most relevant tweets, links, photos and videos for any term from Topsy's index of hundreds of billions of ...
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).
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]