Unsupervised classification of sentiment and objectivity in Chinese text
OPAL (Open@LaTrobe) (La Trobe University)Published 1 January 2008Open access
Taras Zagibalov, John M. Carroll
Citations79
Generate an AI Snapshot to get a quick, structured summary of this paper.
Study Snapshot
ObjectiveStudy objective
MethodsResearch methodology
PopulationPopulation studied
Sample sizeSample sizes
OutcomesStudy outcomes here
ResultsStudy results comes here
LimitationsResearch study limitations comes here
A concise AI-generated summary of the paper will appear here once you click Generate AI Snapshot.
TL;DR
Novel unsupervised techniques are used, including a one-word 'seed' vocabulary and iterative retraining for sentiment processing, and a criterion of 'sentiment density' for determining the extent to which a document is opinionated.
Abstract
We address the problem of sentiment and objectivity classification of product reviews in Chinese. Our approach is distinctive in that it treats both positive / negative sentiment and subjectivity / objectivity not as distinct classes but rather as a continuum; we argue that this is desirable from the perspective of would-be customers who read the reviews. We use novel unsupervised techniques, including a one-word 'seed' vocabulary and iterative retraining for sentiment processing, and a criterion of 'sentiment density' for determining the extent to which a document is opinionated. The classifier achieves up to 87% F-measure for sentiment polarity detection.
Keywords
Computer Science
Thumbs up?
6,987 Citations2002Bo Pang, Lillian Lee +1 more
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.
Meeting of the Association for Computational LinguisticsThumbs Up or Thumbs Down? Semantic Orientation Applied to Unsupervised Classification of Reviews
3,654 Citations2002Peter Peter, Turney
A sentimental education
3,343 Citations2004Bo Pang, Lillian Lee
A novel machine-learning method is proposed that applies text-categorization techniques to just the subjective portions of the document, which greatly facilitates incorporation of cross-sentence contextual constraints.
SENTIWORDNET: A Publicly Available Lexical Resource for Opinion Mining
2,489 Citations2006Andrea Esuli, Fabrizio Sebastiani
SENTIWORDNET is a lexical resource in which each WORDNET synset is associated to three numerical scores Obj, Pos and Neg, describing how objective, positive, and negative the terms contained in the synset are.
Unsupervised word sense disambiguation rivaling supervised methods
2,408 Citations1995David Yarowsky
An unsupervised learning algorithm for sense disambiguation that, when trained on unannotated English text, rivals the performance of supervised techniques that require time-consuming hand annotations.
Seeing stars
2,127 Citations2005Bo Pang, Lillian Lee
A meta-algorithm is applied, based on a metric labeling formulation of the rating-inference problem, that alters a given n-ary classifier's output in an explicit attempt to ensure that similar items receive similar labels.
Mining the peanut gallery
1,908 Citations2003Kushal Dave, Steve Lawrence +1 more
This work develops a method for automatically distinguishing between positive and negative reviews and draws on information retrieval techniques for feature extraction and scoring, and the results for various metrics and heuristics vary depending on the testing situation.
Towards answering opinion questions
1,051 Citations2003Hong Yu, Vasileios Hatzivassiloglou
A Bayesian classifier for discriminating between documents with a preponderance of opinions such as editorials from regular news stories is presented, and three unsupervised, statistical techniques for the significantly harder task of detecting opinions at the sentence level are described.
Opinion Extraction, Summarization and Tracking in News and Blog Corpora.
477 Citations2006Lun‐Wei Ku, Yuting Liang +1 more
Both news and web blog articles are investigated and algorithms for opinion extraction at word, sentence and document level are proposed, and the issue of relevant sentence selection is discussed, and then topical and opinionated information are summarized.
Meeting of the Association for Computational LinguisticsUsing Emoticons to reduce Dependency in Machine Learning Techniques for Sentiment Classification
375 Citations2005Jonathon, Read
Recent Advances in Natural Language ProcessingCustomizing Sentiment Classifiers to New Domains: a Case Study
370 Citations2005Anthony Aue, Michael Gamon
Four different approaches to customizing a sentiment classification system to a new target domain in the absence of large amounts of labeled data are surveyed and their advantages, disadvantages and performance are discussed.
Lecture notes in computer scienceQuery-Based Summarization of Customer Reviews
19 Citations2007Olga Feiguina, Guy Lapalme
It is shown that NLP techniques can be fruitfully used in this context for helping consumers sort out the mass of information displayed in such contexts as consumer reviews about products that have been posted on specialized web sites.
World knowledge in broad-coverage information filtering
15 Citations2007Bennett A. Hagedorn, Massimiliano Ciaramita +1 more
An exploratory study on the problem of classifying financial news stories streamed through RSS feeds focuses on news stories titles, because humans seems to be capable of performing such a task effortlessly on the basis of the little information provided by the title, even with no specific domain expertise.