Implicit Feature Identification via Co-occurrence Association Rule Mining

Hai, Zhen; Chang, Kuiyu; Kim, Jung‐Jae

doi:10.1007/978-3-642-19400-9_31

Cited by 140 publications

(62 citation statements)

References 15 publications

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“…Therefore, in order to be able to find the most effective set of rules, Liu et al [14] employ a greedy algorithm and simulated annealing to select rules. In addition, Hai et al [31] use association rules to mine potential rules that exist between features and sentiment words. They use association rule mining based on the co-occurrence matrix to mine the relationship between opinions and explicit feature words and form more robust rules by clustering explicit feature words.…”

Section: Related Workmentioning

confidence: 99%

An Extended HITS Algorithm on Bipartite Network for Features Extraction of Online Customer Reviews

2018

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How to acquire useful information intelligently in the age of information explosion has become an important issue. In this context, sentiment analysis emerges with the growth of the need of information extraction. One of the most important tasks of sentiment analysis is feature extraction of entities in consumer reviews. This paper first constitutes a directed bipartite feature-sentiment relation network with a set of candidate features-sentiment pairs that is extracted by dependency syntax analysis from consumer reviews. Then, a novel method called MHITS which combines PMI with weighted HITS algorithm is proposed to rank these candidate product features to find out real product features. Empirical experiments indicate the effectiveness of our approach across different kinds and various data sizes of product. In addition, the effect of the proposed algorithm is not the same for the corpus with different proportions of the word pair that includes the "bad", "good", "poor", "pretty good", "not bad" these general collocation words.

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Section: Related Workmentioning

confidence: 99%

An Extended HITS Algorithm on Bipartite Network for Features Extraction of Online Customer Reviews

2018

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“…Even though the paper attempts to give a more aspect level detailed approach, it lacks in dealing with multi words like "hard disk", "battery life" which introduces a significant amount of error in their results. To overcome the significant challenge faced in [1] with respect to category detection, the authors of [3] have taken an algorithm, being an adapted version of [4], is inspired by the work of [5] and [6]. The work in the literature also attempts to identify the implicit aspect words i.e., aspects that are not mentioned directly, unlike the explicit aspects which are mentioned literally in [1].The above would not have been able to identify the 'anecdotes/miscellaneous' type of aspects because they were not directly mentioned in a sentence.…”

Section: Literature Reviewmentioning

confidence: 99%

A comprehensive survey of research based on extraction of opinion words and opinion targets from customer reviews

Kaur¹,

Bandyopadhyay²,

Sowmiya³

2018

IJET

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This survey paper categorizes, compares, and summarizes the algorithms, data sets and performance measurement in the published articles related to extraction of opinion targets and words from customer reviews. The systems reviewed either deploy a supervised or completely unsupervised algorithm for the process. Most of the systems rely on K-nearest neighbor algorithm or a bootstrapping approach. Most of the methods have produced a list comprising of opinion targets from the customer reviews of a product. As a result, opinion targets usually are product features or attributes. The approaches mentioned in the papers reviewed suffer either from error propagation or from lack of automation for parsing long span relations. Certain approaches have taken an initial bag of seed words and proceeded to exploit the syntactical relationship between opinion words and targets. Mainly online product reviews have been used. It is generally believed that the co-occurrence of certain target and opinion words in close proximity makes them more relevant to each other and to the product as well. Owing to the nature of online review data sets most literature have not include assessment of opinion targets and words across multiple domains.

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“…The associations among clusters were weighted based on frequency to explore implicit aspects through reliable links. Two-phase association rule mining (ARM) is used to map implicit aspects to their corresponding explicit aspects using sentiment words as condition (Hai et al 2011). The rule consequents (explicit aspects) are used to generate more conditions for ARM, where implicit aspects could be mined.…”

Section: Frequency and Relation-based Techniquesmentioning

confidence: 99%

Lifelong aspect extraction from big data: knowledge engineering

Khan

Durrani

Khalid

et al. 2016

Complex Adapt Syst Model

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Traditional machine learning techniques follow a single shot learning approach. It includes all supervised, semi-supervised, transfer learning, hybrid and unsupervised techniques having a single target domain known prior to analysis. Learning from one task is not carried to the next task, therefore, they cannot scale up to big data having many unknown domains. Lifelong learning models are tailored for big data having a knowledge module that is maintained automatically. The knowledge-base grows with experience where knowledge from previous tasks helps in current task. This paper surveys topic models leading the discussion to knowledge-based topic models and lifelong learning models. The issues and challenges in learning knowledge, its abstraction, retention and transfer are elaborated. The state-of-the art models store word pairs as knowledge having positive or negative co-relations called must-links and cannot-links. The need for innovative ideas from other research fields is stressed to learn more varieties of knowledge to improve accuracy and reveal more semantic structures from within the data.

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Implicit Feature Identification via Co-occurrence Association Rule Mining

Cited by 140 publications

References 15 publications

An Extended HITS Algorithm on Bipartite Network for Features Extraction of Online Customer Reviews

An Extended HITS Algorithm on Bipartite Network for Features Extraction of Online Customer Reviews

A comprehensive survey of research based on extraction of opinion words and opinion targets from customer reviews

Lifelong aspect extraction from big data: knowledge engineering

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