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- Keyword
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- Certainty Identification (NLP) (2)
- Natural language processing (2)
- Analysis of editorials (1)
- Annotating opinions; newspaper article analysis (1)
- Automation (1)
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- Certainty (1)
- Certainty categorization model (1)
- Certainty expressions; levels of certainty (1)
- Certainty identification (1)
- Circumplex Theory of Affect (1)
- Doubt (1)
- Emotions (1)
- Epistemic comments (1)
- Evidentials (1)
- Explicit certainty information (1)
- Hedges (1)
- Hedging (1)
- Information extraction (1)
- Information retrieval; uncertainty (1)
- Linguistics (1)
- Manual categorization (1)
- Manual tagging (1)
- NLP (1)
- Point of view (1)
- Sentiment Analysis (NLP) (1)
- Social psychology (1)
- Subjectivity (1)
Articles 1 - 3 of 3
Full-Text Articles in Library and Information Science
Certainty Categorization Model, Elizabeth Liddy, Noriko Kando, Victoria Rubin
Certainty Categorization Model, Elizabeth Liddy, Noriko Kando, Victoria Rubin
Victoria Rubin
We present a theoretical framework and preliminary results for manual categorization of explicit certainty information in 32 English newspaper articles. The explicit certainty markers were identified and categorized according to the four hypothesized dimensions – perspective, focus, timeline, and level of certainty. One hundred twenty one sentences from sample news stories contained a significantly lower frequency of markers per sentence (M=0.46, SD =0.04) than 564 sentences from sample editorials (M=0.6, SD =0.23), p= 0.0056, two-tailed heteroscedastic t-test. Within each dimension, editorials had most numerous markers per sentence in high level of certainty, writer’s point of view, and future and present …
Discerning Emotions In Texts, Victoria Rubin, Jeffrey Stanton, Elizabeth Liddy
Discerning Emotions In Texts, Victoria Rubin, Jeffrey Stanton, Elizabeth Liddy
Victoria Rubin
We present an empirically verified model of discernable emotions, Watson and Tellegen’s Circumplex Theory of Affect from social and personality psychology, and suggest its usefulness in NLP as a potential model for an automation of an eight-fold categorization of emotions in written English texts. We developed a data collection tool based on the model, collected 287 responses from 110 non-expert informants based on 50 emotional excerpts (min=12, max=348, average=86 words), and analyzed the inter-coder agreement per category and per strength of ratings per sub-category. The respondents achieved an average 70.7% agreement in the most commonly identified emotion categories per text. …
Certainty Identification In Texts: Categorization Model And Manual Tagging Results, Elizabeth Liddy, Victoria Rubin, Noriko Kando
Certainty Identification In Texts: Categorization Model And Manual Tagging Results, Elizabeth Liddy, Victoria Rubin, Noriko Kando
Victoria Rubin
This chapter presents a theoretical framework and preliminary results for manual categorization of explicit certainty information in 32 English newspaper articles. Our contribution is in a proposed categorization model and analytical framework for certainty identification. Certainty is presented as a type of subjective information available in texts. Statements with explicit certainty markers were identified and categorized according to four hypothesized dimensions – level, perspective, focus, and time of certainty. The preliminary results reveal an overall promising picture of the presence of certainty information in texts, and establish its susceptibility to manual identification within the proposed four-dimensional certainty categorization analytical framework. …