Mining explicit and implicit opinions from reviews

Lazhar, Farek; Tlili, Yamina

doi:10.1504/ijdmmm.2016.075966

Cited by 16 publications

(3 citation statements)

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“…Because unsupervised methods do not require data annotation for implicit features or any sort of training, they are the most frequently used methods for feature extraction in previous research works. Commonly used methods for unsupervised implicit feature extraction include dependency parsing (Zainuddin et al 2016), association rule mining (Mankar and Ingle 2015), ontology (Lazhar and Yamina, 2017), topic modeling (Rana et al 2018), co-occurrence (Prasojo et al 2015), and rule-based (Wan et al 2018). Liao et al (2019) focused on the recognition of fact-implied implicit sentiment at the sentence level.…”

Section: Product Feature Extractionmentioning

confidence: 99%

Feature-Level Sentiment Analysis Based on Rules and Fine-Grained Domain Ontology

Wei¹,

Liu²,

Wei³

2020

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Mining product reviews and sentiment analysis are of great significance, whether for academic research purposes or optimizing business strategies. We propose a feature-level sentiment analysis framework based on rules parsing and fine-grained domain ontology for Chinese reviews. Fine-grained ontology is used to describe synonymous expressions of product features, which are reflected in word changes in online reviews. First, a semiautomatic construction method is developed by using Word2Vec for fine-grained ontology. Then, feature-level sentiment analysis that combines rules parsing and the fine-grained domain ontology is conducted to extract explicit and implicit features from product reviews. Finally, the domain sentiment dictionary and context sentiment dictionary are established to identify sentiment polarities for the extracted feature-sentiment combinations. An experiment is conducted on the basis of product reviews crawled from Chinese e-commerce websites. The results demonstrate the effectiveness of our approach.

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Section: Product Feature Extractionmentioning

confidence: 99%

Feature-Level Sentiment Analysis Based on Rules and Fine-Grained Domain Ontology

Wei¹,

Liu²,

Wei³

2020

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“…Dalam penelitian ABSA terdapat dua aspek, yaitu aspek eksplisit dan aspek implisit [10]. Aspek eksplisit adalah aspek yang disebutkan secara langsung dalam kalimat opini.…”

Section: Pendahuluan Penelitian DI Bidang Sentimen Analisis Berbasunclassified

Penentuan Aspek Implisit dengan Ekstraksi Knowledge Berbasis Rule pada Ulasan Bahasa Indonesia (Determination of Implicit Aspects with Rule Based Knowledge Extraction in Indonesian Reviews)

Setiowati

Setyorini

Helen

2020

JNTETI

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Determination of implicit aspects is one of the important things in opinion sentences. This study proposes a new approach for developing rule-based knowledge by forming a relation between opinion words with aspect categories. The relationship is obtained from the combination of rules, based on Opinion Word Similarity (OWS). Evaluation for rule-based knowledge extraction is in the form of threshold values of frequency and confidence to produce the best precision, recall, and f-measure values. The knowledge extraction consists of two phases: training phase and testing phase. The training phase is described as the process to extract rule-based knowledge. The testing phase is described as the process to obtain the implicit aspects of opinion sentences by referring to rule-based knowledge. To extract rule-based knowledge on user reviews, it is necessary to identify opinion sentences with explicit aspects and get pairs of aspects and words of opinion with rules generated from regular expressions. The evaluation res ult of rule-based knowledge with confidence using OWS showed better results compared to rule-based knowledge without using OWS. By using OWS, precision value increased by 0.25%, recall value increased by 1.15%, and precision value increased by 0.83%.

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“…In Lazhar and Yamina (2016), the challenging problem of implicit opinions has been mentioned. Authors attempted to identify implicit features using ontology semantic relationships and opinion words as clues, and to improve their feature-level sentiment classification approach based on domain ontologies.…”

Section: Related Workmentioning

confidence: 99%

Implicit feature identification for opinion mining

Lazhar

2019

IJBIS

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In opinion mining area, mining consumer reviews can give a finer-grained understanding of consumer needs, which can efficiently help companies and merchants to improve the quality of their products and services. However, identifying features on which consumers express their opinions and sentiments is not always a simple task. Some existing approaches that attempt to extract implicit features using opinion words as clues or co-occurrence techniques lead to unsatisfactory results, and that due to the ambiguity caused by common opinion words which are often expressed on various features. In this paper, we propose an approach based on Association Rule Mining (ARM) and classification techniques. The first step consists of creating from a corpus, a set of association rules regrouping explicit feature-opinion pairs. The second step consists to use this set to build a classification model able to predict for each given set of opinion words the appropriate target. Tested on many classifiers, experimental results show that our approach performs better when incorporating many opinion words rather than using single ones.

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Mining explicit and implicit opinions from reviews

Cited by 16 publications

References 0 publications

Feature-Level Sentiment Analysis Based on Rules and Fine-Grained Domain Ontology

Feature-Level Sentiment Analysis Based on Rules and Fine-Grained Domain Ontology

Penentuan Aspek Implisit dengan Ekstraksi Knowledge Berbasis Rule pada Ulasan Bahasa Indonesia (Determination of Implicit Aspects with Rule Based Knowledge Extraction in Indonesian Reviews)

Implicit feature identification for opinion mining

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