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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.
For example, his paper with Poria S, Cambria E, Howard N, and Huang G-B, on "Fusing audio, visual and textual clues for sentiment analysis from multimodal content", published in (Elsevier) Neurocomputing 174: 50-59 (2016), is an ISI highly cited paper.
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 ]
WordStat is a content analysis and text mining software. [1] It was first released in 1998 after being developed by Normand Peladeau from Provalis Research.The latest version 9 was released in 2021.
One of his key innovations for this purpose is the development of computational stylistic analysis using systemic functional linguistics. [ 16 ] [ 17 ] For example, together with Jeff Dodick and Paul Chase, he examined whether there are clear and consistent differences between scientific method in experimental sciences and historical sciences.
Word2vec was created, patented, [7] and published in 2013 by a team of researchers led by Mikolov at Google over two papers. [1] [2] The original paper was rejected by reviewers for ICLR conference 2013. It also took months for the code to be approved for open-sourcing. [8] Other researchers helped analyse and explain the algorithm. [4]
Speech analysis is an effective method of identifying affective state, having an average reported accuracy of 70 to 80% in research from 2003 and 2006. [ 17 ] [ 18 ] These systems tend to outperform average human accuracy (approximately 60% [ 14 ] ) but are less accurate than systems which employ other modalities for emotion detection, such as ...
Voyant Tools is an open-source, web-based application for performing text analysis. It supports scholarly reading and interpretation of texts or corpus, particularly by scholars in the digital humanities , but also by students and the general public.