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Automatic Detection of Text Genre
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This article propose a theory of genres as bundles of facets, which correlate with various surface cues, and argue that genre detection based on surface cues is as successful as detection by deeper structural properties.Abstract:
As the text databases available to users become larger and more heterogeneous, genre becomes increasingly important for computational linguistics as a complement to topical and structural principles of classification. We propose a theory of genres as bundles of facets, which correlate with various surface cues, and argue that genre detection based on surface cues is as successful as detection based on deeper structural properties.read more
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Opinion Mining and Sentiment Analysis
Bo Pang,Lillian Lee +1 more
TL;DR: This survey covers techniques and approaches that promise to directly enable opinion-oriented information-seeking systems and focuses on methods that seek to address the new challenges raised by sentiment-aware applications, as compared to those that are already present in more traditional fact-based analysis.
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Mining and summarizing customer reviews
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TL;DR: This research aims to mine and to summarize all the customer reviews of a product, and proposes several novel techniques to perform these tasks.
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Thumbs up? Sentiment Classification using Machine Learning Techniques
TL;DR: This work considers the problem of classifying documents not by topic, but by overall sentiment, e.g., determining whether a review is positive or negative, and concludes by examining factors that make the sentiment classification problem more challenging.
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The Text Mining Handbook: Advanced Approaches in Analyzing Unstructured Data
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TL;DR: Providing an in-depth examination of core text mining and link detection algorithms and operations, this text examines advanced pre-processing techniques, knowledge representation considerations, and visualization approaches.
Proceedings ArticleDOI
Learning extraction patterns for subjective expressions
Ellen Riloff,Janyce Wiebe +1 more
TL;DR: A bootstrapping process that learns linguistically rich extraction patterns for subjective (opinionated) expressions while maintaining high precision is presented.
References
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Book
Generalized Linear Models
Peter McCullagh,John A. Nelder +1 more
TL;DR: In this paper, a generalization of the analysis of variance is given for these models using log- likelihoods, illustrated by examples relating to four distributions; the Normal, Binomial (probit analysis, etc.), Poisson (contingency tables), and gamma (variance components).
Book
Variation across Speech and Writing
TL;DR: The model applied in this study addressed textual dimensions and relations in speech and writing, as well as situations and functions, and its application to linguistic research on speech andWriting.
Book
Dimensions of Register Variation: A Cross-Linguistic Comparison
TL;DR: The linguistic bases of cross-linguistic register comparisons: a detailed quantitative comparison of English and Somali registers and multi-dimensional analyses of the four languages are presented.
Journal ArticleDOI
Spoken and Written Textual Dimensions in English: Resolving the Contradictory Findings
Book
Backpropagation: the basic theory
TL;DR: Since the publication of the PDP volumes in 1986, learning by backpropagation has become the most popular method of training neural networks because of the underlying simplicity and relative power of the algorithm.