Hybrid approach to extract adjectives for implicit aspect identification in opinion mining

Hannach, Hajar El; Benkhalifa, Mohammed

doi:10.1109/sita.2016.7772284

Cited by 9 publications

(1 citation statement)

References 11 publications

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“…Schouten and Frasincar (2014) labeled the dataset with implicit features and computed the co-occurrence score between the labeled implicit features and other dictionaries. Hajar and Mohammed (2016) used a hybrid approach of the labeled corpus, WordNet, and Naive Bayes classifier for implicit feature extraction.…”

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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Non-negative matrix factorization for implicit aspect identification

Zhu

Dai

et al. 2019

J Ambient Intell Human Comput

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A Systematic Review on Implicit and Explicit Aspect Extraction in Sentiment Analysis

et al. 2020

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Aspect-based sentiment analysis (ABSA) is currently among the most vigorous areas in natural language processing (NLP). Individuals, private and government institutions are increasingly using media sources for decision making. In the last decade, aspect extraction has been the most essential phase of sentiment analysis (SA) to conduct an abridged sentiment classification. However, previous studies on sentiment analysis mostly focused on explicit aspects extraction with limited work on implicit aspects. To the best of our knowledge, this is the first systematic review that covers implicit, explicit, and the combination of both implicit and explicit aspect extractions. Therefore, this systematic review has been conducted to, 1) identify techniques used for extracting implicit, explicit, or both implicit and explicit aspects; 2) analyze the various evaluation metrics, data domains, and languages involved in the implicit and explicit aspect extraction in sentiment analysis from years 2008 to 2019; 3) identify the key challenges associated with the techniques based on the result of a comprehensive comparative analysis; and finally, 4) highlight the feasible opportunities for future research directions. This review can be used to assist novice and prominent researchers to understand the concept of both implicit and explicit aspect extractions in aspect-based sentiment analysis domain. INDEX TERMS aspect-based sentiment analysis, aspect extraction, explicit aspect, feature extraction, implicit aspect.

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Hybrid approach to extract adjectives for implicit aspect identification in opinion mining

Cited by 9 publications

References 11 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

Non-negative matrix factorization for implicit aspect identification

A Systematic Review on Implicit and Explicit Aspect Extraction in Sentiment Analysis

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