Mining and summarizing customer reviews

By Minqing Hu, Bing Liu. 2004

8,541 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems, 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

1. Mohit Bansal
2. Jianfeng Gao
3. Percy Liang
4. Luke S. Zettlemoyer
5. Noah A. Smith
6. Yejin Choi
7. Graham Neubig
8. Erik Cambria

In Information Systems

1. Yongfeng Zhang
2. Xiangnan He
3. Xu Chen
4. B. Liu
5. Bing Liu
6. Charu C. Aggarwal
7. Philip S. Yu
8. Saif M. Mohammad

In Natural Language Processing

1. Mohit Bansal
2. Luke S. Zettlemoyer
3. Jianfeng Gao
4. Graham Neubig
5. Yejin Choi
6. Percy Liang
7. Noah A. Smith
8. Christopher D. Manning

Citations in the corpus

• Layer Normalization (2016, background): “…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) [Pang and Lee, 2004] and opinion polarity (MPQA) [Wiebe et al., 2005].”
• GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding (2018, neutral): “, 2013), CR (Hu and Liu, 2004), and SUBJ (Pang and Lee, 2004).”
• An efficient framework for learning sentence representations (2018, methodology): “The following classification benchmarks are commonly used: movie review sentiment (MR) (Pang & Lee, 2005), product reviews (CR) (Hu & Liu, 2004), subjectivity classification (SUBJ) (Pang & Lee, 2004), opinion polarity (MPQA) (Wiebe et al., 2005), question type classification (TREC) (Voorhees &…”
• Learning Distributed Representations of Sentences from Unlabelled Data (2016, background): “(MR) (Pang and Lee, 2005), product reviews (CR) (Hu and Liu, 2004), subjectivity classification (SUBJ) (Pang and Lee, 2004), opinion polarity (MPQA) (Wiebe et al.”
• Skip-Thought Vectors (2015, methodology): “on several classification benchmarks which are commonly used for evaluating sentence representation learning methods. We use 5 datasets: movie review sentiment (MR) [36], customer product reviews (CR) [37], subjectivity/objectivity classification (SUBJ) [38], opinion polarity (MPQA) [39] and question-”
• Reducing Sentiment Bias in Language Models via Counterfactual Evaluation (2019, background): “7% validation accuracy, and iii) a simple opinion-word-based sentiment classifier, which counts the number of positive opinion words p and the number of negative opinion words n (Hu and Liu, 2004) and derives its sentiment score as p/(p + n), and 0.”
• SentEval: An Evaluation Toolkit for Universal Sentence Representations (2018, background): “…sentiment analysis (MR and both binary and fine-grained SST) (Pang and Lee, 2005; Socher et al., 2013), question-type (TREC) (Voorhees and Tice, 2000), product reviews (CR) (Hu and Liu, 2004), subjectivity/objectivity (SUBJ) (Pang and Lee, 2004) and opinion polarity (MPQA) (Wiebe et al., 2005).”
• 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].”
• Self-training Improves Pre-training for Natural Language Understanding (2020, background): “, 2013) binary and fine-grained sentiment analysis datasets (SST-2 and SST-5), on product classification (CR) from (Hu and Liu, 2004), hate-speech comment classification3 (IMP), question classification (TREC) from (Voorhees and Tice, 2000) and named entity recognition (CoNLL 2002) from (Sang and De ”
• Convolutional Neural Networks for Sentence Classification (2014, neutral): “Task is to predict positive/negative reviews (Hu and Liu, 2004).”
• Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks (2019, background): “uct reviews (Hu and Liu, 2004).”
• Examining Gender and Race Bias in Two Hundred Sentiment Analysis Systems (2018, background): “, 2014; Mohammad, 2018), AFINN (Nielsen, 2011), and Bing Liu Lexicon (Hu and Liu, 2004).”
• Learning to Generate Reviews and Discovering Sentiment (2017, methodology): “On the MR (Pang & Lee, 2005) and CR (Hu & Liu, 2004) sentiment analysis datasets we improve the state of the art by a significant margin.”
• Deep Unordered Composition Rivals Syntactic Methods for Text Classification (2015, methodology): “We use the lexicon created by Hu and Liu (2004), which consists of 2,006 positive words and 4,783 negative words.”
• Universal Sentence Encoder (2018, methodology): “CR : Sentiment of sentences mined from customer reviews (Hu and Liu, 2004).”