Rated aspect summarization of short comments

By Yue Lu, Chengxiang Zhai, Neel Sundaresan. The Web Conference, 2009

414 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems

View Paper: https://www.semanticscholar.org/paper/87eeb5622d8fbe4dca5f1c9b4190f719818c4d6e

How researchers see this paper

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

In Artificial Intelligence

In Information Systems

Citations in the corpus

Automatic construction of a context-aware sentiment lexicon: an optimization approach (2011, methodology): “Yet another kind of approaches exploit the association between words and expression-level or document-level sentiment [2, 12, 20].”
Improving the quality of predictions using textual information in online user reviews (2013, background): “The approach in [25] identifies aspects or topics by clustering phrases in textual comments, and identifies user sentiment towards these aspects.”
A Survey of Opinion Mining and Sentiment Analysis (2012, background): “[66] defined a problem of rated aspect summarization.”
Learning Attitudes and Attributes from Multi-aspect Reviews (2012, background): “Some more recent works that deal with multi-aspect rating systems are [1], [5], [12], [23], [22].”
Latent aspect rating analysis on review text data: a rating regression approach (2010, background): “Recent work by Lu et al. [17] is the closest to ours, but their goal is still to generate from overall ratings.”
Hidden factors and hidden topics: understanding rating dimensions with review text (2013, background): “Furthermore, although related to our work, the goal of such studies is typically summarization [5, 13, 18], or feature discovery [22], rather than rating prediction per se .”
Cross-domain sentiment classification via spectral feature alignment (2010, background): “As a result, opinion mining has attracted much attention recently [29, 24], for example, opinion summarization [19, 26], opinion integration [25] and review spam identiﬁcation [21], etc. Sentiment classiﬁcation, which aims at classifying sentiment data into polarity categories (e.g., positive or…”
Opinosis: A Graph Based Approach to Abstractive Summarization of Highly Redundant Opinions (2010, background): “Most existing work in Opin-ion Summarization focus on predicting sentiment orientation on an entity (Pang et al., 2002) (Pang and Lee, 2004) or attempt to generate aspect-based ratings for that entity (Snyder and Barzilay, 2007) (Lu et al., 2009)(Lerman et al., 2009)(Titov and Mcdonald, 2008).”
Aspect Extraction through Semi-Supervised Modeling (2012, background): “aspects and sentiments (Lu et al., 2009), and modeling attitudes (Sauper et al.”
Mining Aspect-Specific Opinion using a Holistic Lifelong Topic Model (2016, neutral): “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].”
Latent aspect rating analysis without aspect keyword supervision (2011, background): “Other work on finer granularity analysis of opinions expressed in review text content is mostly to analyze the aspectlevel opinion orientations [25, 6, 7, 20, 4, 15].”
Feature-based transfer learning with real-world applications (2010, background): “In sentiment analysis, there are some interesting tasks, for example, opinion extraction and summarization [81, 118, 184, 93], opinion integration [117], review spam identification [88], review quality prediction [116, 114] and opinion analysis in multiple languages [1], etc.”