Creating Subjective and Objective Sentence Classifiers from Unannotated Texts

By Janyce Wiebe, Ellen Riloff. Conference on Intelligent Text Processing and Computational Linguistics, 2005

659 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/879e4dd192cdaa71a63f05eb1317380d8ad7f041

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In Artificial Intelligence

Citations in the corpus

Self-Training for End-to-End Speech Recognition (2019, methodology): “Self-training has been applied to tasks in natural language processing including word-sense disambiguation [2], noun identification [23] and parsing [3], in addition to tasks in computer vision such as object detection [24, 25] and image classification [26].”
Neural Models of Factuality (2018, background): “, 2008), MPQA Opinion Corpus (Wiebe and Riloff, 2005), the LU corpus of author belief commitments (Diab et al.”
Combining lexicon-based and learning-based methods for twitter sentiment analysis (2011, methodology): “For example, Wiebe and Riloff (2005) used a subjectivity lexicon to identify training data for supervised learning for subjectivity classification.”
Opinion Mining and Sentiment Analysis (2008, methodology): “) The performance of this simple method is on par with that of a method based on a state-of-the-art subjectivity detection system, OpinionFinder [256, 315].”
Approaches, Tools and Applications for Sentiment Analysis Implementation (2015, background): “According to [1] an opinion “is simply a positive or negative sentiment, view, attitude, emotion, or appraisal about an entity or an aspect of the entity” from an opinion holder at a specific time [2; 3].”
OpinionFinder: A System for Subjectivity Analysis (2005, background): “Please see (Wiebe and Riloff, 2005; Choi et al., 2005; Wilson et al., 2005) for discussions of related work in automatic opinion and sentiment analysis.”
A survey of sentiment analysis in social media (2018, background): “[136] proposed a rule-based classifier, which classifies a sentence with strong subjective clues into a subjective category.”
Bayesian network based extreme learning machine for subjectivity detection (2017, methodology): “Similar to the MPQA experiment, a 5-fold cross validation has been used to estimate he performance of the trained classifier.”
Recognizing Contextual Polarity in Phrase-Level Sentiment Analysis (2005, background): “Sentence Features:These are features that were found useful for sentence-level subjectivity classification by Wiebe and Riloff (2005).”
Toward Automated Fact-Checking: Detecting Check-worthy Factual Claims by ClaimBuster (2017, background): “, [2, 25, 27]) which classifies sentences into objective and subjective categories.”
Identifying Sources of Opinions with Conditional Random Fields and Extraction Patterns (2005, methodology): “HM is a more conservative measure that considers an extraction to be correct if its head matches the head of the annotated source.”
Evaluating sentiment in financial news articles (2012, methodology): “It was developed by Wiebe's group based on a series of publications, such as the subjective sentence classifier (Riloff and Wiebe, 2003; Wiebe and Riloff, 2005), and the polarity classifier (Wilson, Wiebe et al., 2005b).”
Sentiment Analysis: An Overview from Linguistics (2016, positive): “Janyce Wiebe and colleagues have annotated corpora with expressions of opinion (Wiebe et al 2005), and have developed classifiers to distinguish objective from subjective sentences (Wiebe & Riloff 2005).”
Sentiment Analysis: Detecting Valence, Emotions, and Other Affectual States from Text (2016, methodology): “One of the earliest problems tackled in sentiment analysis is that of detecting subjective language (Wiebe, Wilson, Bruce, Bell, & Martin, 2004; Wiebe & Riloff, 2005).”