Web Data Mining: Exploring Hyperlinks, Contents, and Usage Data. Second Edition

By B. Liu. Data-Centric Systems and Applications, 2009

1,563 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/3055134761ca2e7748d7d5325d70231d3391d847

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

Citations in the corpus

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).”
Sentiment Analysis and Opinion Mining (2012, background): “In their case, they only extracted aspects from Pros and Cons of review format 2 as in Liu et al. (2005). They also clustered those synonym aspects and ranked aspects based on their frequency and their contributions to the overall review rating of reviews.”
Helpfulness of Online Consumer Reviews: Readers' Objectives and Review Cues (2012, methodology): “We conducted Web data mining in October 2010 with the aim of discovering useful information from Web hyperlinks, page contents, and usage logs [35].”
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 usergenerated content, such as microblogs, reviews, etc.”
Social Media Mining: An Introduction (2014, methodology): “General references for sentiment analysis can be found in [Pang and Lee, 2008; Liu, 2007], and examples of recent developments in social media are available in [Hu et al.”
Performance Analysis of IoT-Based Sensor, Big Data Processing, and Machine Learning Model for Real-Time Monitoring System in Automotive Manufacturing (2018, background): “Based on a confusion matrix [78], the prediction output can have four possible outcomes, as can be seen in Table 6.”
A Survey of Opinion Mining and Sentiment Analysis (2012, background): “Below, we list some of them, which we call the basic rules of opinions [58, 59].”
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).”
SemEval-2015 Task 12: Aspect Based Sentiment Analysis (2015, background): “Following Liu (2006) & Zhang and Liu (2014), an aspect (term or category) indicated: (a) a part/component of e (e.”
Sampling Content from Online Social Networks (2015, methodology): “Several algorithms have been proposed for inferring sentiment from English text [Liu 2006].”
Large scale integration of senses for the semantic web (2009, methodology): “We can consider it as an agglomerative clustering technique [14].”