Extracting Product Features and Opinions from Reviews

Popescu, Ana-Maria; Etzioni, Orena

doi:10.1007/978-1-84628-754-1_2

Cited by 417 publications

(250 citation statements)

References 15 publications

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“…The work in [15] is representative in this regard and describes the use of shallow natural language processing (NLP) techniques for explicit feature extraction and sentiment analysis; see also [16,17]. The features extracted, and the techniques used, are similar to those presented in this chapter, although in the case of the former there was a particular focus on the extraction of merenomic and taxonomic features to describe the parts and properties of a product.…”

Section: Mining Opinions and Features From User-generated Reviewsmentioning

confidence: 98%

Case-Studies in Mining User-Generated Reviews for Recommendation

Dong

O’Mahony

McCarthy

et al. 2015

Advances in Social Media Analysis

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User-generated reviews are now plentiful online and they have proven to be a valuable source of real user opinions and real user experiences. In this chapter we consider recent work that seeks to extract topics, opinions, and sentiment from review text that is unstructured and often noisy. We describe and evaluate a number of practical case-studies for how such information can be used in an information filtering and recommendation context, from filtering helpful reviews to recommending useful products.

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Section: Mining Opinions and Features From User-generated Reviewsmentioning

confidence: 98%

Case-Studies in Mining User-Generated Reviews for Recommendation

Dong

O’Mahony

McCarthy

et al. 2015

Advances in Social Media Analysis

View full text Add to dashboard Cite

show abstract

“…This algorithm consists of applying association rule mining to identify features, pruning uninteresting and redundant features, identifying infrequent features and finally determining semantic orientation of each opinion sentence. Popescu and Etzioni created an unsupervised system for feature and opinion extraction from product reviews [3]. After finding an explicit feature in a sentence, they applied manually crafted extraction rules to the sentence and extracted the heads of potential opinion phrases.…”

Section: Customer Review Feature and Polarity Extractionmentioning

confidence: 99%

“…The problem of automatic reviews analysis and classification has attracted much attention due to its importance in ecommerce applications [1,2,3]. Recently, there is an increasing number of sites where users rate doctors.…”

Section: Introductionmentioning

confidence: 99%

Automatic Classification of Online Doctor Reviews: Evaluation of Text Classifier Algorithms (Preprint)

Rivas¹,

Montazeri²,

Le³

et al. 2018

Preprint

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Background: An increasing number of doctor reviews are being generated by patients on the internet. These reviews address a diverse set of topics (features), including wait time, office staff, doctor's skills and bedside manner. Most previous work on automatic analysis of online customer reviews assumes that (a) product features are described unambiguously by a small number of keywords, e.g., "battery" for phones, and (b) the opinion for each feature has positive or negative sentiment. However, in the domain of doctor reviews, this setting is too restrictive: a feature such as "visit duration" for doctor reviews may be expressed in many ways, and does not necessarily have positive/negative sentiment.

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“…9 Although good progress has been made in these tasks, 10,11 there are still some problems that need to be solved. This paper focused on product element extracting and tendency judgment automatically from the massive product review data using NLP.…”

Section: Related Workmentioning

confidence: 99%

Fine-Grained Emotion Analysis Based on Mixed Model for Product Review

Sun¹,

Sun²,

Quan³

et al. 2017

IJNDC

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Nowadays, with the rapid development of B2C e-commerce and the popularity of online shopping, the Web storages huge number of product reviews comment by customers. A large number of reviews made it difficult for manufacturers or potential customers to track the comments and suggestions that customers made. This paper presents a method for extracting emotional elements containing emotional objects and emotional words and their tendencies from product reviews based on mixed model. First we constructed conditional random fields to extract emotional elements, lead-in semantic and word meaning as features to improve the robustness of feature template and used rules for hierarchical filtering errors. Then we constructed support vector machine to classify the emotional tendency of the fine-grained elements to achieve key information from product reviews. Deep semantic information imported based on neural network to improve the traditional bag of word model. Experimental results show that the proposed model with deep features efficiently improved the F-Measure.

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Extracting Product Features and Opinions from Reviews

Cited by 417 publications

References 15 publications

Case-Studies in Mining User-Generated Reviews for Recommendation

Case-Studies in Mining User-Generated Reviews for Recommendation

Automatic Classification of Online Doctor Reviews: Evaluation of Text Classifier Algorithms (Preprint)

Fine-Grained Emotion Analysis Based on Mixed Model for Product Review

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