An Unsupervised Approach to Product Attribute Extraction

Bapi, Raju S.; Pingali, Prasad; Varma, Vasudeva

doi:10.1007/978-3-642-00958-7_88

Cited by 36 publications

(22 citation statements)

References 2 publications

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“…Santosh Raju and Katharina Probst proposed the method of extracting attributes and attribute-value pairs from the product description [4][5]. The method of product attributes extraction was unsupervised and semi-supervised.…”

Section: Methods Of Extract Attributementioning

confidence: 99%

Extracting Attributes of Named Entity from Unstructured Text with Deep Belief Network

Zhong¹,

Liu²,

Du³

et al. 2016

IJDTA

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Section: Methods Of Extract Attributementioning

confidence: 99%

Extracting Attributes of Named Entity from Unstructured Text with Deep Belief Network

Zhong¹,

Liu²,

Du³

et al. 2016

IJDTA

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“…Popescu et al extracted [3] frequent noun phrases and identified potential features from them using OPINE system. Raju et al extracted [4] product featured from the clustered noun phrases describing same feature. They used Attribute score formula to identify the product features.…”

Section: Related Workmentioning

confidence: 99%

A Combined Approach for Effective Features Extraction from Online Product Reviews

Santosh¹

2018

IJEME

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Today E-commerce websites provide customers with the needed product information by giving various kinds of services to choose from. One such service is to allow the customer to read the end user online reviews. Online reviews contain features which are useful for the analysis in opinion mining. Converting these unstructured reviews into useful information require extracting the product features from them. Natural Language Processing (NLP) based technique extracts various kinds of product features including the low frequency features. Topic Modeling based approach also identifies specific product features from the online reviews. The effective number of product features is made available to the customer when these two approaches are combined. This results in the expanded product feature set so that the customer makes wise decisions without having to compromise on the feature set.

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“…In both of these works, high-frequency noun phrases are extracted as candidates and then filtered using a scoring function. Raju et al [12] apply group average agglomerative clustering based on the number of ngrams in common between the words. Topic modelling approaches such as Titov and McDonald [1] and Brody and Elhadad [13] treat each aspect as fine-grained distributions over the words in the reviews.…”

Section: Related Workmentioning

confidence: 99%

Discovering aspects of online consumer reviews

Suleman

Vechtomova

2015

Journal of Information Science

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In this paper we propose a fully unsupervised approach for product aspect discovery in on-line consumer reviews. We apply a two-step hierarchical clustering process in which we first cluster words representing aspects based on the semantic similarity of their contexts and then on the similarity of the hypernyms of the cluster members. Our approach also includes a method for assigning class labels to each of the clusters. We evaluated our methods on large datasets of restaurant and camera reviews and found that the two-step clustering process performed better than a single-step clustering process at identifying aspects and words refering to aspects. Finally, we compare our method to a state-of-the-art topic modelling approach by Titov and McDonald, and demonstrate better results on both datasets.

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An Unsupervised Approach to Product Attribute Extraction

Cited by 36 publications

References 2 publications

Extracting Attributes of Named Entity from Unstructured Text with Deep Belief Network

Extracting Attributes of Named Entity from Unstructured Text with Deep Belief Network

A Combined Approach for Effective Features Extraction from Online Product Reviews

Discovering aspects of online consumer reviews

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