Identifying comparative sentences in text documents

By Nitin Jindal, B. Liu. Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 2006

530 citations recorded by the source corpus.

Subfields: Artificial Intelligence

View Paper: https://www.semanticscholar.org/paper/864d8d57112be5c7c65efcac3da9c932a01bf4fb

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In Artificial Intelligence

Citations in the corpus

Latent aspect rating analysis on review text data: a rating regression approach (2010, background): “to alleviate this problem including extracting information from reviews [18, 16, 26], summarizing users’ opinions, categorizing reviews according to opinion polarities [20, 6, 7], and extracting comparative sentences from reviews [12, 13].”
Sentiment analysis: A review and comparative analysis of web services (2015, background): “On the other hand, comparative sentences express the resemblance between entities considering common aspects or features [32,33,100].”
Opinion Mining and Sentiment Analysis (2008, background): “Examples of the types of relations particularly pertinent to opinion mining are those centered around comparisons — consider, for example, the relations encoded by such sentences as “The new model is more expensive than the old one” or “I prefer product A over product B” [139, 191, longer version of”
Survey on Aspect-Level Sentiment Analysis (2016, background): “the entities and aspects that are compared, as well as the comparative words themselves is very useful [83].”
An Interpretable Classification Framework for Information Extraction from Online Healthcare Forums (2017, methodology): “Finally, we obtain a set of FLSPs, which can be used as features to identify the relationship between labels and patterns in sentences [12].”
Mining Comparative Sentences and Relations (2006, methodology): “More details about this step can be found in (Jindal and Liu 2006)”
Sentiment Analysis and Subjectivity (2010, methodology): “It is shown in [38] that using a set of 83 keywords and key phrases, 98% of the comparative sentences (recall = 98%) can be identified with a precision of 32% using the authors’ data set.”
A Survey of Opinion Mining and Sentiment Analysis (2012, background): “” In [36], Jindal and Liu identified a list of such words.”
Detection of Harassment on Web 2.0 (2009, background): “Extensive work [5, 4, 7, 6] has been done in mining user opinions on products.”
Latent aspect rating analysis without aspect keyword supervision (2011, background): “Latent Aspect Rating Analysis (LARA) refers to the task of inferring both opinion ratings on topical aspects (e.g., location, service of a hotel) and the relative weights reviewers have placed on each aspect based on review content and the associated overall ratings.”
Computational approaches for mining user's opinions on the Web 2.0 (2014, background): “…opinion mining Hu & Liu, 2004b; Liu, Hu, & Cheng, 2005) is about analysis of sentiment regarding certain properties of objects (e.g. Hu & Liu, 2004a); (3) comparison-based opinion mining deals with texts in which comparisons of similar objects are made (e.g. Jindal & Liu, 2006a, 2006b).”
A review of natural language processing techniques for opinion mining systems (2017, background): “Two types of rules, Class Sequential Rules (CSRs) [127] and Label Sequential Rules (LSRs) are designed to deal with the two sub-tasks, respectively.”
Effective Pattern Discovery for Text Mining (2012, background): “In the presence of these set backs, sequential patterns used in data mining community have turned out to be a promising alternative to phrases [13], [50] because sequential patterns enjoy good statistical properties like terms.”
Overview of Touché 2022: Argument Retrieval (2022, unknown): “Comparative sentences are then identified and mined from product reviews in favor or against one or the other product using opinion mining approaches [39, 40, 42].”