Extracting Opinion Targets in a Single and Cross-Domain Setting with Conditional Random Fields

By Niklas Jakob, Iryna Gurevych. Conference on Empirical Methods in Natural Language Processing, 2010

532 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/4d135641931a6efce82bd9c1d69d86e08d3cd28d

How researchers see this paper

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

1. Soujanya Poria
2. B. Liu
3. Bing Liu
4. Hu Xu
5. Erik Cambria
6. Lei Shu
7. Zixuan Ke
8. Arjun Mukherjee

Citations in the corpus

• Computational approaches for mining user's opinions on the Web 2.0 (2014, unknown): “…latent semantic association (e.g. Guo, Zhu, Guo, & Su, 2011; Guo, Zhu, Guo, Zahng, & Su, 2009; Hofmann, 2001) up to combinations and variations of existing algorithms (Airoldi, Bai, & Padman, 2006; Choi, Cardie, Riloff, & Patwardhan, 2005; Jakob & Gurevych, 2010; Nakagawa, Inui, & Kurohashi, 2010).”
• A Rule-Based Approach to Aspect Extraction from Product Reviews (2014, methodology): “Niklas and Gurevych (Niklas and Gurevych, 2010) used CRF to extract explicit aspects in a custom corpus with data of different domains.”
• Cross-Domain Co-Extraction of Sentiment and Topic Lexicons (2012, methodology): “Cross-Domain CRF (Cross-CRF) we implement a cross-domain CRF algorithm proposed by (Jakob and Gurevych, 2010).”
• Lifelong Learning CRF for Supervised Aspect Extraction (2017, methodology): “Dependency relations have been shown useful in many sentiment analysis applications (Johansson and Moschitti, 2010; Jakob and Gurevych, 2010).”
• Aspect extraction for opinion mining with a deep convolutional neural network (2016, methodology): “Most of the previous works in aspect term extraction have either used conditional random fields (CRFs) (Jakob and Gurevych, 2010; Zhiqiang and Wenting, 2014) or linguistic patterns (Hu and Liu, 2004; Poria et al., 2015b).”
• Grammatical structures for word-level sentiment detection (2012, background): “The most recent target identification techniques use machine learning to determine the presence of a target from known opinionated language (Jakob and Gurevych, 2010).”
• Survey on Aspect-Level Sentiment Analysis (2016, background): “…aspect detection precision: 78% / 77% recall: 56% / 64% phone & LCD forum posts precision: 68% / 66% recall: 44% / 55% supervised machine learning Jakob & Gurevych (2010) [35] data from [36][37][38] product, hotel, and aspect detection no quantitative evaluation restaurant reviews Lu et al.…”
• Aspect Extraction through Semi-Supervised Modeling (2012, methodology): “Another approach is to use supervised sequence labeling (Liu, Hu and Cheng 2005; Jin and Ho, 2009; Jakob and Gurevych, 2010; Li et al., 2010; Choi and Cardie, 2010; Kobayashi et al., 2007; Yu et al., 2011).”
• Automated Rule Selection for Aspect Extraction in Opinion Mining (2015, methodology): “On the statistical approach, we will show that our method outperforms the CRF-based method [Jakob and Gurevych, 2010] by a large margin.”
• Double Embeddings and CNN-based Sequence Labeling for Aspect Extraction (2018, methodology): “Aspect extraction has been performed using supervised (Jakob and Gurevych, 2010; Chernyshe-vich, 2014; Shu et al., 2017) and unsupervised approaches (Hu and Liu, 2004; Zhuang et al., 2006; Mei et al., 2007; Qiu et al., 2011; Yin et al., 2016; He et al., 2017).”