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20022005
most citedThumbs up? Sentiment Classification using Machine Learning Techniques

2.2k citations

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7 papers · 1 filter

cs.CL2005727 cited

Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales

Bo Pang, Lillian Lee

We address the rating-inference problem, wherein rather than simply decide whether a review is "thumbs up" or "thumbs down", as in previous sentiment analysis work, one must determ…

cs.CL20045 cited

A Framework for Creating Natural Language User Interfaces for Action-Based Applications

Stephen Chong, Riccardo Pucella

In this paper we present a framework for creating natural language interfaces to action-based applications. Our framework uses a number of reusable application-independent componen…

cs.CL2004660 cited

A Sentimental Education: Sentiment Analysis Using Subjectivity Summarization Based on Minimum Cuts

Bo Pang, Lillian Lee

Sentiment analysis seeks to identify the viewpoint(s) underlying a text span; an example application is classifying a movie review as "thumbs up" or "thumbs down". To determine thi…

cs.CL200398 cited

Learning to Paraphrase: An Unsupervised Approach Using Multiple-Sequence Alignment

Regina Barzilay, Lillian Lee

We address the text-to-text generation problem of sentence-level paraphrasing -- a phenomenon distinct from and more difficult than word- or phrase-level paraphrasing. Our approach…

cs.CL20022.2k cited

Thumbs up? Sentiment Classification using Machine Learning Techniques

Bo Pang, Lillian Lee, Shivakumar Vaithyanathan

We consider the problem of classifying documents not by topic, but by overall sentiment, e.g., determining whether a review is positive or negative. Using movie reviews as data, we…

cs.CL20026 cited

Bootstrapping Lexical Choice via Multiple-Sequence Alignment

Regina Barzilay, Lillian Lee

An important component of any generation system is the mapping dictionary, a lexicon of elementary semantic expressions and corresponding natural language realizations. Typically,…