Annotating Expressions of Opinions and Emotions in Language

By Janyce Wiebe, Theresa Wilson, Claire Cardie. 2005

2,095 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Natural Language Processing

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Natural Language Processing

Citations in the corpus

Layer Normalization (2016, background): “We checkpoint both models after every 50,000 iterations and evaluate their performance on ﬁve tasks: semantic-relatedness (SICK) [Marelli et al., 2014], movie review sentiment (MR) [Pang and Lee, 2005], customer product reviews (CR) [Hu and Liu, 2004], subjectivity/objectivity classiﬁcation (SUBJ) [”
GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding (2018, background): “Other tasks are so close to being solved that evaluation on them is relatively uninformative, such as MPQA (Wiebe et al., 2005) and TREC question classiﬁcation (Voorhees et al., 1999).”
An efficient framework for learning sentence representations (2018, neutral): “The reported performance is an average over all the classification benchmarks (MSRP, TREC, MR, CR, SUBJ, MPQA).”
Learning Distributed Representations of Sentences from Unlabelled Data (2016, background): “Representations are applied to 6 sentence classi-ﬁcation tasks: paraphrase identiﬁcation (MSRP) (Dolan et al., 2004), movie review sentiment (MR) (Pang and Lee, 2005), product reviews (CR) (Hu and Liu, 2004), subjectivity classiﬁcation (SUBJ) (Pang and Lee, 2004), opinion polarity (MPQA) (Wiebe et a”
Making Pre-trained Language Models Better Few-shot Learners (2021, methodology): “Task Auto template Auto label words SST-2 (positive/negative) <S1> A [MASK] one . irresistible/pathetic <S1> A [MASK] piece . wonderful/bad <S1> All in all [MASK] . delicious/bad SST-5 (very positive/positive/neutral/negative/very negative) <S1> The movie is [MASK] . wonderful/remarkable/hilarious/b”
Representation Learning with Contrastive Predictive Coding (2018, methodology): “For the classiﬁcation tasks we used the following datasets: movie review sentiment (MR) [43], customer product reviews (CR) [44], subjectivity/objectivity [45], opinion polarity (MPQA) [46] and question-type classiﬁcation (TREC) [47].”
Convolutional Neural Networks for Sentence Classification (2014, methodology): “tude larger than listed in table 1. 5http://cogcomp.cs.illinois.edu/Data/QA/QC/ 6http://www.cs.uic.edu/˘liub/FBS/sentiment-analysis.html MPQA: Opinion polarity detection subtask of the MPQA dataset (Wiebe et al., 2005).7 3.1 Hyperparameters and Training For all datasets we use: rectiﬁed linear units”
Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks (2019, background): “• MPQA: Phrase level opinion polarity classification from newswire (Wiebe et al., 2005).”
Dynabench: Rethinking Benchmarking in NLP (2021, methodology): “In future iterations, we plan to consider additional dimensions of sentiment and emotional expression (Alm et al., 2005; Neviarouskaya et al., 2010; Wiebe et al., 2005; Liu et al., 2003; Sudhof et al., 2014).”
Universal Sentence Encoder (2018, background): “MPQA : Phrase level opinion polarity from news data (Wiebe et al., 2005).”
A Sensitivity Analysis of (and Practitioners’ Guide to) Convolutional Neural Networks for Sentence Classification (2015, neutral): “(7) MPQA : Opinion polarity dataset (Wiebe et al., 2005).”
Universal Sentence Encoder for English (2018, methodology): “(MPQA) Phrase opinion polarity from news data (Wiebe et al., 2005); (TREC) Fine grained question classification sourced from TREC (Li and Roth, 2002); (SST) Binary phrase sentiment classification (Socher et al., 2013); (STS Benchmark) Semantic textual similarity (STS) between sentence pairs scored…”
Cyber Hate Speech on Twitter: An Application of Machine Classification and Statistical Modeling for Policy and Decision Making (2015, background): “This is a n Op e n Acces s doc u m e n t dow nloa d e d fro m ORCA, Ca r diff U nive r si ty 's ins ti t u tion al r e posi to ry: h t t p s://o rc a .c a r diff.ac.uk/id/e p rin t/73 1 5 8/ This is t h e a u t ho r’s ve r sion of a wo rk t h a t w as s u b mi t t e d to / a c c e p t e d for p u…”
SimCSE: Simple Contrastive Learning of Sentence Embeddings (2021, methodology): “We also experiment with post-processing methods (BERT- 16https://github.com/facebookresearch/ SentEval Model MR CR SUBJ MPQA SST TREC MRPC Avg.”
Good debt or bad debt: Detecting semantic orientations in economic texts (2013, methodology): “In our paper, the deﬁnition of these entities builds on the lexicon derived from the Multi-perspective Question Answering (MPQA) corpus of opinion annotations (Wiebe et al., 2005; Wilson, 2008; Wilson et al., 2009).”