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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.
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 ...
Sentiment may refer to: Feelings, and emotions; Public opinion, also called sentiment; Sentimentality, an appeal to shallow, uncomplicated emotions at the expense of reason; Sentimental novel, an 18th-century literary genre; Market sentiment, optimism or pessimism in financial and commodity markets
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.
Natural language generation (NLG) is a software process that produces natural language output. A widely-cited survey of NLG methods describes NLG as "the subfield of artificial intelligence and computational linguistics that is concerned with the construction of computer systems that can produce understandable texts in English or other human languages from some underlying non-linguistic ...
Abstractive summarization methods generate new text that did not exist in the original text. [12] This has been applied mainly for text. Abstractive methods build an internal semantic representation of the original content (often called a language model), and then use this representation to create a summary that is closer to what a human might express.
Network analysis, sentiment analysis 2004 (2015) [36] [37] Klimt, B. and Y. Yang Ling-Spam Dataset Corpus containing both legitimate and spam emails. Four version of the corpus involving whether or not a lemmatiser or stop-list was enabled. 2,412 Ham 481 Spam Text Classification 2000 [38] [39] Androutsopoulos, J. et al. SMS Spam Collection Dataset
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.