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| Sentiment Analysis in Communication× | Sentiment Analysis× | |
|---|---|---|
| Field≠ | Communication | Text mining |
| Family | Process / pipeline | Process / pipeline |
| Year of origin≠ | 2010 | — |
| Originator≠ | Adapted into communication research from NLP / opinion mining | — |
| Type≠ | Automated classification of message valence/tone | NLP text-classification task |
| Seminal source≠ | Tausczik, Y. R., & Pennebaker, J. W. (2010). The psychological meaning of words: LIWC and computerized text analysis methods. Journal of Language and Social Psychology, 29(1), 24–54. DOI ↗ | Pang, B. & Lee, L. (2008). Opinion Mining and Sentiment Analysis. Foundations and Trends in Information Retrieval, 2(1-2), 1-135. DOI ↗ |
| Aliases≠ | Opinion mining in communication, Tone analysis, Media sentiment analysis, İletişimde Duygu Analizi | opinion mining, polarity detection, duygu analizi |
| Related≠ | 5 | 3 |
| Summary≠ | Sentiment analysis is the automated estimation of the valence — positive, negative, or neutral tone — of communication messages, adapted from natural-language processing into a core measurement technique for media and communication research. It lets scholars quantify the tone of news coverage, the affect of social-media discourse, or audience reactions across corpora far too large for hand coding, while treating tone as a measurable, validatable construct. | Sentiment analysis, also called opinion mining, is a natural-language-processing task that detects the emotional tone of text — typically classifying it as positive, negative, or neutral. It turns unstructured opinion text into structured, quantifiable polarity signals using one of three families of approaches: sentiment lexicons, trained machine-learning classifiers, or pretrained transformer models. |
| ScholarGateDataset ↗ |
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