Words with attitude
By Jaap Kamps, Maarten Marx. 2002
165 citations recorded by the source corpus.
View Paper: https://www.semanticscholar.org/paper/d36d9edfca333ed245fa4d069090b211ceb165f8
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
- Rada Mihalcea
- Kumar Ravi
- Vadlamani Ravi
- Erik Boiy
- Xiaojun Wan
- B. Liu
- Janyce Wiebe
- Marie-Francine Moens
Citations in the corpus
- A machine learning approach to sentiment analysis in multilingual Web texts (2009, methodology): “Kamps and Marx (2002) use WordNet to determine the orientation of a word.”
- Text Analysis for Automatic Image Annotation (2007, methodology): “We determine the visualness for every synset us-ing a method that was inspired by Kamps and Marx (2002).”
- Articles: Recognizing Contextual Polarity: An Exploration of Features for Phrase-Level Sentiment Analysis (2009, methodology): “Research ranges from work on learning the prior polarity (semantic orientation) of words and phrases (e.g., Hatzivassiloglou and McKeown 1997; Kamps and Marx 2002; Turney and Littman 2003; Hu and Liu 2004; Kim and Hovy 2004; Esuli and Sebastiani 2005; Takamura, Inui, and Okumura 2005; Popescu and…”
- A survey on opinion mining and sentiment analysis: Tasks, approaches and applications (2015, background): “semantic differentiation with WordNet [9], and topic proximity and syntactic-relation features [171].”
- Word Sense and Subjectivity (2006, methodology): “Second, a number of methods for subjectivity or sentiment analysis start with a set of seed words and then search through WordNet to find other subjective words (Kamps and Marx, 2002; Yu and Hatzivassiloglou, 2003; Hu and Liu, 2004; Kim and Hovy, 2004; Esuli and Sebastiani, 2005).”
- Sentiment Analysis using Support Vector Machines with Diverse Information Sources (2004, methodology): “…past, work has been done in the area of characterizing words and phrases according to their emotive tone (Turney and Littman, 2003; Turney, 2002; Kamps et al., 2002; Hatzivassiloglou and Wiebe, 2000; Hatzi-vassiloglou and McKeown, 2002; Wiebe, 2000), but in many domains of text, the values of…”
- Movie review mining and summarization (2006, methodology): “edu) based approach [7], using semantic distance from a word to “good” and “bad” in WordNet as the classification criterion.”