Predicting the semantic orientation of adjectives

Hatzivassiloglou, Vasileios; McKeown, Kathleen

doi:10.3115/979617.979640

Cited by 758 publications

(631 citation statements)

References 4 publications

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“…Although many approaches to subjectivity classification focus only on the presence of subjectivity cue words themselves, disregarding context (e.g., Hart (1984), Anderson and McMaster (1982), Hatzivassiloglou and McKeown (1997), Turney (2002), Gordon et al (2003), Yi et al (2003)), the observations in this section suggest that different usages of words, in context, need to be distinguished to understand subjectivity.…”

Section: Ambiguity Of Individual Wordsmentioning

confidence: 95%

“…Certainly much of the work on identifying subjective expressions in NLP has focused on learning adjectives (e.g., Hatzivassiloglou and McKeown (1997), Wiebe (2000), and Turney (2002)). Among the content words (as defined above) in expressive subjective elements, 14% are adverbs, 21% are verbs, 27% are adjectives, and 38% are nouns.…”

Section: Wide Variety Of Words and Parts Of Speechmentioning

confidence: 99%

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Annotating Expressions of Opinions and Emotions in Language

2005

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This paper describes a corpus annotation project to study issues in the manual annotation of opinions, emotions, sentiments, speculations, evaluations and other private states in language. The resulting corpus annotation scheme is described, as well as examples of its use. In addition, the manual annotation process and the results of an inter-annotator agreement study on a 10,000-sentence corpus of articles drawn from the world press are presented.

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Section: Ambiguity Of Individual Wordsmentioning

confidence: 95%

Section: Wide Variety Of Words and Parts Of Speechmentioning

confidence: 99%

Annotating Expressions of Opinions and Emotions in Language

2005

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“…Prior work has shown that it is possible to generate and extend polarity dictionaries in an unsupervised manner using grammatical [31] or co-occurrence relations [83] between words. By applying these methods on Web data, we can also infer the polarity for slang and common misspellings [84], which improves the quality of opinion mining on, e.g., social media data.…”

Section: Opinion Mining For Culturomicsmentioning

confidence: 99%

Visions and open challenges for a knowledge-based culturomics

et al. 2015

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The concept of culturomics was born out of the availability of massive amounts of textual data and the interest to make sense of cultural and language phenomena over time. Thus far however, culturomics has only made use of, and shown the great potential of, statistical methods. In this paper, we present a vision for a knowledge-based culturomics that complements traditional culturomics. We discuss the possibilities and challenges of combining knowledgebased methods with statistical methods and address major challenges that arise due to the nature of the data; diversity of sources, changes in language over time as well as temporal dynamics of information in general. We address all layers needed for knowledge-based culturomics, from natural language processing and relations to summaries and opinions.

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“…Subjectivity analysis is defined by Wiebe in (1994), "linguistic expression of somebody's opinions, sentiments, emotions, evaluations, beliefs and speculations" (Wiebe 1994). Hatzivassiloglou and McKeown (1997) analyzed the semantic constraints of conjunction in large-scale corpus to calculate the emotional tendency of adjectives (Hatzivassiloglou and McKeown 1997).…”

Section: Introductionmentioning

confidence: 99%

Hierarchical classification in text mining for sentiment analysis of online news

Fong

Zhuang

et al. 2015

Soft Comput

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Sentiment analysis in text mining is a challenging task. Sentiment is subtly reflected by the tone and affective content of a writer's words. Conventional text mining techniques, which are based on keyword frequencies, usually run short of accurately detecting such subjective information implied in the text. In this paper, we evaluate several popular classification algorithms, along with three filtering schemes. The filtering schemes progressively shrink the original dataset with respect to the contextual polarity and frequent terms of a document. We call this approach "hierarchical classification". The effects of the approach in different combination of classification algorithms and filtering schemes are discussed over three sets of controversial online news articles where binary and multi-class classifications are applied. Meanwhile we use two methods to test this hierarchical classification model, and also have a comparison of the two methods.

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Predicting the semantic orientation of adjectives

Cited by 758 publications

References 4 publications

Annotating Expressions of Opinions and Emotions in Language

Annotating Expressions of Opinions and Emotions in Language

Visions and open challenges for a knowledge-based culturomics

Hierarchical classification in text mining for sentiment analysis of online news

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