Aspect Extraction through Semi-Supervised Modeling

By Arjun Mukherjee, B. Liu. 2012

381 citations recorded by the source corpus.

How researchers see this paper

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

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

Citations in the corpus

• Recent Trends in Deep Learning Based Natural Language Processing (2017, background): “Tasks involving sentiment analysis also require effective extraction of aspects along with their sentiment polarities [49].”
• Aspect extraction for opinion mining with a deep convolutional neural network (2016, methodology): “In the category of supervised methods, (Jagarlamudi et al., 2012) employed seed words to guide topic models to learn topics of specific interest to a user, while (Wang et al., 2010) and (Mukherjee and Liu, 2012) employed seeding words to extract related product aspects from product reviews.”
• Lifelong-RL: Lifelong Relaxation Labeling for Separating Entities and Aspects in Opinion Targets (2016, background): “…Wang, 2008; Wu et al., 2009; Lin and He, 2009; Zhang et al., 2010; Mei et al., 2007; Li et al., 2010; Brody and Elhadad, 2010; Wang et al., 2010; Mukherjee and Liu, 2012; Fang and Huang, 2012; Zhou et al., 2013; Liu et al., 2013; Poria et al., 2014), we are not aware of any attempt to solve the…”
• Recent Trends in Deep Learning Based Natural Language Processing [Review Article] (2018, background): “august 2018 | ieee Computational intelligenCe magazine 61 Tasks involving sentiment analysis also require effective extraction of aspects along with their sentiment polarities [48].”
• Aspect-Controllable Opinion Summarization (2021, background): “…in the literature such as sentiment analysis (Pang et al., 2002), aspect extraction (Hu and Liu, 2004; He et al., 2017), combinations thereof (Mukherjee and Liu, 2012; Pontiki et al., 2016), and notably opinion summarization (Hu and Liu, 2006; Wang and Ling, 2016), whose aim is to create a…”
• Automated Rule Selection for Aspect Extraction in Opinion Mining (2015, background): “…the latter on CRF [Jakob and Gurevych, 2010; Choi and Cardie, 2010; Mitchell et al. , 2013] and topic modeling [Mei et al. , 2007; Titov and McDonald, 2008; Li et al. , 2010; Brody and El-hadad, 2010; Moghaddam and Ester, 2011; Sauper et al. , 2011; Mukherjee and Liu, 2012; Chen et al. , 2014 ] .”
• Aspect-Oriented Opinion Mining from User Reviews in Croatian (2013, background): “While early attempts focused on classifying overall document opinion (Turney, 2002; Pang et al., 2002), more recent approaches identify opinions expressed about individual product aspects (Popescu and Etzioni, 2007; Fahrni and Klenner, 2008; Mukherjee and Liu, 2012).”
• Mining Aspect-Specific Opinion using a Holistic Lifelong Topic Model (2016, methodology): “There are also many topic models that have been used for the task of aspect extraction and categorization in, for example, [4, 11, 24, 28, 49, 27, 32, 33, 43, 10, 5, 14, 22, 23, 30, 45, 46].”
• An Unsupervised Neural Attention Model for Aspect Extraction (2017, methodology): “1 ; for SAS and BTM, α = 50 /K and β = 0 .”
• Sentiment Analysis and Opinion Mining (2012, methodology): “4 GROUP SPAM DETECTION An initial group spam detection algorithm was proposed in Mukherjee et al. (2011), which was improved in Mukherjee et al.”
• Exploiting Topic based Twitter Sentiment for Stock Prediction (2013, background): “…in its extended forms in sentiment analysis and many other tasks (Mei et al., 2007; Branavan et al., 2008; Lin and He, 2009; Zhao et al., 2010; Wang et al., 2010; Brody and Elhadad, 2010; Jo and Oh, 2011; Moghaddam and Ester, 2011; Sauper et al., 2011; Mukherjee and Liu, 2012; He et al., 2012).”
• Topic Modeling using Topics from Many Domains, Lifelong Learning and Big Data (2014, background): “The most related work to this paper is the knowledge-based topic models (KBTM), (e.g., Andrzejewski et al. 2009; Chen et al. 2013b; Mukherjee & Liu 2012).”
• Current State of Text Sentiment Analysis from Opinion to Emotion Mining (2017, methodology): “For instance, one of the most important group of works in this category is devoted to utilizing topic modeling in aspect extraction such as the work of Lin and He [2009], Jo and Oh [2011], Mukherjee and Liu [2012], and Wang et al. [2016].”