Web data mining: exploring hyperlinks, contents, and usage data

By Olfa Nasraoui. SKDD, 2008

414 citations recorded by the source corpus.

This paper presents a review of the book "Web Data Mining - Exploring Hyperlinks, Contents, and Usage Data" by Bing Liu. The review concludes that the breadth and depth of this book makes it a required staple for every Web mining researcher, student, or practitioner.

Subfields: Information Systems, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/128c265e3f4ae5db480149101a7b84e78c76921c

How researchers see this paper

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

In Information Systems

In Statistical and Nonlinear Physics

Citations in the corpus

Recommender Systems (2016, neutral): “Manually defined dictionaries are available for phrase extraction, although automated methods can also be used [144, 364, 400].”
Mining Comparative Sentences and Relations (2006, methodology): “The mechanism behind this is quite involved (see (Jindal & Liu 2006; Liu 2006) for details).”
Top 10 algorithms in data mining (2007, background): “G. J. McLachlan Department of Mathematics, The University of Queensland, Brisbane, Australia e-mail: gjm@maths.uq.edu.au A. Ng School of Medicine, Griffith University, Brisbane, Australia B. Liu Department of Computer Science, University of Illinois at Chicago, Chicago, IL 60607, USA P. S. Yu IBM T.”
Recursive Neural Conditional Random Fields for Aspect-based Sentiment Analysis (2016, background): “Aspect-based sentiment analysis (Pang and Lee, 2008; Liu, 2011) aims to extract important information, e.g. opinion targets, opinion expressions, target categories, and opinion polarities, from user-generated content, such as microblogs, reviews, etc.”
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…”
Opinion Word Expansion and Target Extraction through Double Propagation (2011, background): “In this mining task, opinion targets usually refer to product features, which are deﬁned as product components or attributes, as in Liu (2006).”
Sentiment Knowledge Discovery in Twitter Streaming Data (2010, neutral): “Considering sentiment analysis [18, 21], O’Connor et al.”
Extracting and Ranking Product Features in Opinion Documents (2010, methodology): “Besides precision and recall, we adopt the precision@N metric for experimental evaluation (Liu, 2006).”
Computational approaches for mining user's opinions on the Web 2.0 (2014, methodology): “A variety of algorithms exist in this subject: k-means, mixture models, hierarchical clustering, etc. (Liu, 2008).”
Feature-based transfer learning with real-world applications (2010, background): “Recently, opinion mining, also known as sentiment analysis, has attracted much attention in the natural language processing and information retrieval areas [110, 145, 111].”
Opinion spam and analysis (2008, background): “In the context of Web search, due to the economic and/or publicity value of the rank position of a page returned by a search engine, Web page spam is widespread [3, 5, 10, 12, 16, 24, 25].”