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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 ]
In modern times [15] "sentimental" is a pejorative term that has been casually applied to works of art and literature that exceed the viewer or reader's sense of decorum—the extent of permissible emotion—and standards of taste: "excessiveness" is the criterion; [16] "Meretricious" and "contrived" sham pathos are the hallmark of sentimentality, where the morality that underlies the work is ...
The underlying technology platform, a natural language processing (NLP) and sentiment analysis system called Lydia, was developed by Dr. Steven Skiena at Stony Brook University. [7] It used Apache Hadoop to process large quantities of data. General Sentiment’s software accurately predicted the winner of the American Idol Finale in 2011. [8]
Such software helps to organize, manage and analyse information. [21] The advantages of using this software include saving time, managing huge amounts of qualitative data, having increased flexibility, having improved validity and auditability of qualitative research, and being freed from manual and clerical tasks.
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 ...
The simplest and most objective form of content analysis considers unambiguous characteristics of the text such as word frequencies, the page area taken by a newspaper column, or the duration of a radio or television program. Analysis of simple word frequencies is limited because the meaning of a word depends on surrounding text.
Sentiment of each sentence has been hand labeled as positive or negative. 3000 Text Classification, sentiment analysis 2015 [100] [101] D. Kotzias BlogFeedback Dataset Dataset to predict the number of comments a post will receive based on features of that post. Many features of each post extracted. 60,021 Text Regression 2014 [102] [103] K. Buza