Opinion observer: analyzing and comparing opinions on the Web
By B. Liu, Minqing Hu, Junsheng Cheng. The Web Conference, 2005
1,892 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Information Systems
View Paper: https://www.semanticscholar.org/paper/2671e9ff0eb68739d6610cf67937e6b34a2fcf10
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In Artificial Intelligence
In Information Systems
Citations in the corpus
- Recommender systems based on user reviews: the state of the art (2015, methodology): “Specifically, they first train a Support Vector Machine (SVM) regression model, which uses opinion features (extracted from reviews by association rule mining (Liu et al. 2005)) and technical specifications (e.”
- Explicit factor models for explainable recommendation based on phrase-level sentiment analysis (2014, background): “The core task in phrase-level sentiment analysis is the construction of Sentiment Lexicon [34, 17, 6, 19], where each entry is a (Feature,Opinion,Sentiment) triplet.”
- Quality evaluation of product reviews using an information quality framework (2011, methodology): “To alleviate this information overload problem, opinion mining and retrieval techniques [5,11,15,20,24] have been devised to extract and retrieve meaningful opinions from reviews.”
- Bilingual Co-Training for Sentiment Classification of Chinese Product Reviews (2011, background): “In recent years, sentiment classification has drawn much attention in the natural language processing (NLP) field and it has many useful applications, such as opinion mining and summarization (Liu, Hu, and Cheng 2005; Ku, Liang, and Chen 2006; Titov and McDonald 2008).”
- Identifying comparative sentences in text documents (2006, background): “The POS tags and their categories that are important to this work are: NN : Noun, NNP : Proper Noun, VBZ : Verb, present tense, 3 rd person singular, JJ : Adjective, RB : Adverb, JJR : adjective, comparative, JJS : adjective, superlative, RBR : Adverb, comparative, RBS : Adverb, superlative.”
- Machine Learning-Based Sentiment Analysis for Twitter Accounts (2018, methodology): “Opinion Lexicon [10] was used to find a total number of positive, neutral and negative tweets.”
- MeanSum: A Neural Model for Unsupervised Multi-Document Abstractive Summarization (2018, background): “Liu et al. (2005); Ly et al.”
- Gender Classification of Weblog Authors (2006, background): “In particular, researchers have attempted to mine writing moods, product opinions, and underlying social networks from blogs (Gruhl et al. 2004; Liu, Hu, & Cheng 2005; Mishne 2005).”
- Movie review mining and summarization (2006, methodology): “To deal with the reviews in a special format, Liu et al expanded the opinion word list by adding some nouns [8].”
- Opinion Mining and Sentiment Analysis (2008, background): “) As another example, a sample output of the Opinion Observer system [191] is depicted in Figure 5.”
- A computational approach to politeness with application to social factors (2013, background): “Terms from the sentiment lexicon (Liu et al., 2005) are also tools for positive politeness, either by emphasizing a positive relationship with the ad-dressee (line 4), or being impolite by using negative sentiment that damages this positive relationship (line 5).”
- Computational approaches for mining user's opinions on the Web 2.0 (2014, positive): “E.g. aspect based opinion mining focuses on feature based approaches (e.g. Hu & Liu, 2004a; Liu et al., 2005; Moghaddam & Ester, 2010), while others apply latent variable models like the hidden Markov model (HMM, e.g. Jin, Ho, & Srihari, 2009; Wong, Bing, & Lam, 2011), conditional random fields…”
- Survey on mining subjective data on the web (2011, background): “The problem of mining product reviews has attracted particular attention in the research community (Morinaga et al. 2002; Dave et al. 2003; Liu et al. 2005; Carenini et al. 2005).”