Mining product reputations on the Web

By Satoshi Morinaga, Kenji Yamanishi, Kenji Tateishi, Toshikazu Fukushima. Knowledge Discovery and Data Mining, 2002

501 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics, Information Systems

View Paper: https://www.semanticscholar.org/paper/4f0d67a3a8a61d3a7ef0f940274ecff5f0d640ea

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

In Artificial Intelligence

In Computational Theory and Mathematics

In Information Systems

Citations in the corpus

Sentiment analysis in multiple languages: Feature selection for opinion classification in Web forums (2008, background): “Reviews include movie, product, and music reviews [Morinaga et al. 2002; Pang et al. 2002; Turney 2002].”
Articles: Recognizing Contextual Polarity: An Exploration of Features for Phrase-Level Sentiment Analysis (2009, background): “Although a great deal of work in sentiment analysis has targeted documents, applications such as opinion question answering (Yu and Hatzivassiloglou 2003; Maybury 2004; Stoyanov, Cardie, and Wiebe 2005) and review mining to extract opinions about companies and products (Morinaga et al. 2002; Nasukaw”
Which Side are You on? Identifying Perspectives at the Document and Sentence Levels (2006, background): “, 2003), and discriminating between positive and negative language (Pang et al., 2002; Morinaga et al., 2002; Yu and Hatzivassiloglou, 2003; Turney and Littman, 2003; Dave et al., 2003; Nasukawa and Yi, 2003; Popescu and Etzioni, 2005; Wilson et al., 2005).”
Determining the semantic orientation of terms through gloss classification (2005, methodology): “The method we present outperforms all known methods when tested on the recognized standard benchmarks for this task.”
Just How Mad Are You? Finding Strong and Weak Opinion Clauses (2004, background): “Others perform automatic analyses of product reputations (Morinaga et al., 2002; Nasukawa and Yi, 2003; Yi et al., 2003).”
Movie Review Mining: a Comparison between Supervised and Unsupervised Classification Approaches (2005, background): “Opinion mining aims to mine reviews of various products (i.e. PDAs, cameras, electronics, cars, books, music records, movie reviews, etc.) by classifying them into positive or negative opinions [1] [2] [4].”
Approaches, Tools and Applications for Sentiment Analysis Implementation (2015, background): “The term sentiment analysis first appeared in [4], however the research on sentiments/opinions appeared earlier [5; 6; 7; 8; 9].”
Mining the peanut gallery: opinion extraction and semantic classification of product reviews (2003, background): “The first is from Opion, now part of Planetfeedback.com [8, 22]. The second is from NEC Japan [ 14 ].”
Opinion Mining and Sentiment Analysis (2008, background): “[215]: principal-components-analysis visualization of associations between products (squares) and automatically-selected opinion-oriented terms (circles).”
Latent aspect rating analysis on review text data: a rating regression approach (2010, background): “However, existing works on aspect-based summarization [10, 21, 18, 26] only aimed at aggregating all the reviews and representing major opinions on diﬀerent aspects for a given topic.”
Sentiment analyzer: extracting sentiments about a given topic using natural language processing techniques (2003, background): “There has been extensive research on automatic text analysis for sentiment, such as sentiment classifiers[13] [6] [16] [2] [19], affect analysis[17] [21], automatic survey analysis[8] [16], opinion extraction[12], or recommender systems [18].”
Sentiment Analysis and Subjectivity (2010, methodology): “The manual approach is very time-consuming [15, 65, 94, 106] and thus it is not usually used alone, but combined with automated approaches as the final check because automated methods make mistakes.”
Sentiment Analysis and Opinion Mining (2012, background): “However, the research on sentiments and opinions appeared earlier (Das and Chen, 2001; Morinaga et al., 2002; Pang et al., 2002; Tong, 2001; Turney, 2002; Wiebe, 2000).”
Creating Subjective and Objective Sentence Classifiers from Unannotated Texts (2005, methodology): “Automatic subjectivity/opinion/sentiment analysis is being applied to many interesting applications, including classification of reviews [19, 14, 11, 25, 26], analysis of product reputations [26, 25, 27], tracking sentiments toward events [28, 22, 29], and incorporating opinions into into question a”
Sentiment analysis: capturing favorability using natural language processing (2003, background): “Except for stemming and analysis of part of speech (POS), they simply analyze co-occurrences of expressions within a short distance [7,12] or patterns [1] that are typically used for information extraction [3,10] to analyze the relationships among expressions.”
Twitter Sentiment Analysis (2015, background): “The ﬁeld of text categorization was initiated long time ago (Salton and McGill, 1983), however categorization based on sentiment was introduced more recently in (Das and Chen, 2001; Morinaga et al., 2002; Pang et al., 2002; Tong, 2001; Turney, 2002; Wiebe, 2000).”