SemEval-2010 task 18: disambiguating sentiment ambiguous adjectives
Language Resources and EvaluationPublished 30 November 2012
Yunfang Wu, Peng Jin
Citations25
SJR quartileQ1
SJR score0.48
SNIP1.79
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
An evaluation task is presented at SemEval-2010, designed to provide a framework for comparing different approaches on sentiment ambiguous adjectives, and provides manually labeled test data.
Abstract
Sentiment ambiguous adjectives, which have been neglected by most previous researches, pose a challenging task in sentiment analysis. We present an evaluation task at SemEval-2010, designed to provide a framework for comparing different approaches on this problem. The task focuses on 14 Chinese sentiment ambiguous adjectives, and provides manually labeled test data. There are 8 teams submitting 16 systems in this task. In this paper, we define the task, describe the data creation, list the participating systems, and discuss different approaches.
Keywords
Computer Science
now publishers, Inc. eBooksOpinion Mining and Sentiment Analysis
6,745 Citations2008Bo Pang, Lillian Lee
Foundations and Trends® in Information RetrievalOpinion Mining and Sentiment Analysis
6,204 Citations2008Bo Pang, Lillian Lee
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.
Recognizing contextual polarity in phrase-level sentiment analysis
3,379 Citations2005Theresa Wilson, Janyce Wiebe +1 more
A new approach to phrase-level sentiment analysis is presented that first determines whether an expression is neutral or polar and then disambiguates the polarity of the polar expressions.
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.
ACM Transactions on Information SystemsMeasuring praise and criticism
1,509 Citations2003Peter D. Turney, Michael L. Littman
This article introduces a method for inferring the semantic orientation of a word from its statistical association with a set of positive and negative paradigm words, based on two different statistical measures of word association.
Determining the sentiment of opinions
1,485 Citations2004Soo-Min Kim, Eduard Hovy
A system that, given a topic, automatically finds the people who hold opinions about that topic and the sentiment of each opinion and another module for determining word sentiment and another for combining sentiments within a sentence is presented.
Predicting the semantic orientation of adjectives
1,439 Citations1997Vasileios Hatzivassiloglou, Kathleen McKeown
A log-linear regression model uses constraints from conjunctions to predict whether conjoined adjectives are of same or different orientations, achieving 82% accuracy in this task when each conjunction is considered independently.
A holistic lexicon-based approach to opinion mining
1,388 Citations2008Xiaowen Ding, Bing Liu +1 more
This paper proposes a holistic lexicon-based approach to solving the problem of determining the semantic orientations (positive, negative or neutral) of opinions expressed on product features in reviews by exploiting external evidences and linguistic conventions of natural language expressions.
Extracting semantic orientations of words using spin model
407 Citations2005Hiroya Takamura, Takashi Inui +1 more
The mean field approximation is used to compute the approximate probability function of the system instead of the intractable actual probability function, and a criterion for parameter selection on the basis of magnetization is proposed.
One sense per collocation
384 Citations1993David Yarowsky
This paper shows that for certain definitions of collocation, a polysemous word exhibits essentially only one sense per collocation and utilizes this property in a disambiguation algorithm that achieves precision of 92% using combined models of very local context.
<i>Retracted December 5, 2011:</i> A novel lexicalized HMM-based learning framework for web opinion mining
329 Citations2009Wei Jin, Hung Hay Ho
Word sense and subjectivity
296 Citations2006Janyce Wiebe, Rada Mihalcea
Empirical evidence is brought in support of the hypotheses that (1) subjectivity is a property that can be associated with word senses, and (2) word sense disambiguation can directly benefit from subjectivity annotations.
Structure-Aware Review Mining and Summarization
284 Citations2010Fangtao Li, Chao Han +5 more
This paper proposes a new machine learning framework based on Conditional Random Fields that can employ rich features to jointly extract positive opinions, negative opinions and object features for review sentences and shows that structure-aware models outperform many state-of-the-art approaches to review mining.
Computational LinguisticsDisambiguating Nouns, Verbs, and Adjectives Using Automatically Acquired Selectional Preferences
141 Citations2003Diana McCarthy, John Carroll
Although the preferences perform well in comparison with other unsupervised WSD systems on the same corpus, the results show that for many applications, further knowledge sources would be required to achieve an adequate level of accuracy and coverage.
SELC
109 Citations2009Likun Qiu, Weishi Zhang +2 more
The standard deviation of F1-scores is reduced, which shows that the SELC Model could be more suitable for domain-independent sentiment classification.
Extracting Semantic Orientations of Phrases from Dictionary
100 Citations2007Hiroya Takamura, Takashi Inui +1 more
Unlike existing methods for phrase classification, the proposed method can classify phrases consisting of unseen words and is proposed to use unlabeled data for a seed set of probability computation.
Meeting of the Association for Computational LinguisticsTwitter Based System: Using Twitter for Disambiguating Sentiment Ambiguous Adjectives
95 Citations2010Alexander Pak, Patrick Paroubek
This system uses text messages from Twitter, a popular microblogging platform, for building a dataset of emotional texts and classifies the meaning of adjectives into positive or negative sentiment polarity according to the given context.
Latent Variable Models for Semantic Orientations of Phrases
95 Citations2006Hiroya Takamura, Takashi Inui +1 more
From words to senses
88 Citations2008Fangzhong Su, Katja Markert
It is shown that results achieve with existing resources that are not tailored towards word sense subjectivity classification can rival results achieved with supervision on a manually annotated training set.
Disambiguating Dynamic Sentiment Ambiguous Adjectives
73 Citations2010Yunfang Wu, Miaomiao Wen
This paper proposes a knowledge-based method to automatically determine the semantic orientation of DSAAs within context, and outperforms previous work in sentiment classification of product reviews.
Principled disambiguation: discriminating adjective senses with modified nouns
70 Citations1995John S. Justeson, Slava M. Katz
This paper argues for a linguistically principled approach to disambiguation, in which relevant contextual clues are narrowly defined, in syntactic and semantic terms, and in which only highly reliable clues are exploited.
Mining the Sentiment Expectation of Nouns Using Bootstrapping Method
39 Citations2011Miaomiao Wen, Yunfang Wu
An unsupervised bootstrapping method is proposed to generate a new type of affect knowledge base: the sentiment expectation of nouns, which could be automatically predicted with the output patterns.
Lecture notes in computer scienceSentiment Tagging of Adjectives at the Meaning Level
12 Citations2006Alina Andreevskaia, Sabine Bergler
A sentiment tagging system which is based on multiple bootstrapping runs on WordNet synsets and glosses using different non-intersecting seed lists of manually annotated words and the addition of a module for partial sense disambiguation of sentiment-bearing adjectives using combinatorial patterns is presented.
Meeting of the Association for Computational LinguisticsOpAL: Applying Opinion Mining Techniques for the Disambiguation of Sentiment Ambiguous Adjectives in SemEval-2 Task 18
10 Citations2010Alexandra Balahur, Andrés Montoyo
The OpAL team's experiments carried out for the participation in the SemEval 2010 Task 18 -- Disambiguation of Sentiment Ambiguous Adjectives yielded good results, the OpAL system run ranking fifth among 16 in micro accuracy and sixth in macro accuracy.
YSC-DSAA: An Approach to Disambiguate Sentiment Ambiguous Adjectives Based on SAAOL
10 Citations2010Shicai Yang, Meijuan Liu
The system developed for the SemEval-2010 task of disambiguating sentiment ambiguous adjectives created a new word library named SAA-Oriented Library consisting of positive words, negative words, positive words related to SAA, and inverse words, etc.
CityU-DAC: Disambiguating Sentiment-Ambiguous Adjectives within Context
8 Citations2010Bin Lu, Benjamin K. Tsou
The results show that the performance of maximum entropy is not quite high due to little training data; on the other hand, the lexicon-based method could improve the precision by considering the polarity of context.
HITSZ_CITYU: Combine Collocation, Context Words and Neighboring Sentence Sentiment in Sentiment Adjectives Disambiguation
6 Citations2010Ruifeng Xu, Jun Xu +1 more
The HIT_CITYU systems in Semeval-2 Task 18 are presented, namely, disambiguating sentiment ambiguous adjectives and collocation set expansion and characteristics learning based on word similarity and semisupervised learning are investigated, respectively.