Review-aggregated aspect-based sentiment analysis with ontology features

Kok, Sophie de; Punt, Linda; Puttelaar, Rosita van den; Ranta, Karoliina; Schouten, Kim; Frăsincar, Flavius

doi:10.1007/s13748-018-0163-7

Cited by 25 publications

(10 citation statements)

References 18 publications

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“…Sophie de Lok et al, using ontology features proposed an aspect-based sentiment analysis on restaurant reviews data evolved from SemEval 2016 [13]. They defined 3 main ontology classes like Entity extraction, Generic positive/negative, sentiment.…”

Section: Related Workmentioning

confidence: 99%

ABSA: Computational Measurement Analysis Approach for Prognosticated Aspect Extraction System

Syamala¹,

Nalini²

2021

TEM Journal

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Aspect based sentient analysis (ABSA) is identified as one of the current research problems in Natural Language Processing (NLP). Traditional ABSA requires manual aspect assignment for aspect extraction and sentiment analysis. In this paper, to automate the process, a domain-independent dynamic ABSA model by the fusion of Efficient Named Entity Recognition (E-NER) guided dependency parsing technique with Neural Networks (NN) is proposed. The extracted aspects and sentiment terms by E-NER are trained to a Convolutional Neural Network (CNN) using Word embedding’s technique. Aspect categorybased polarity prediction is evaluated using NLTK Vader Sentiment package. The proposed model was compared to traditional rule-based approach, and the proposed dynamic model proved to yield better results by 17% when validated in terms of correctly classified instances, accuracy, precision, recall and F-Score using machine learning algorithms.

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Section: Related Workmentioning

confidence: 99%

ABSA: Computational Measurement Analysis Approach for Prognosticated Aspect Extraction System

Syamala¹,

Nalini²

2021

TEM Journal

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“…They used the ontology to identify appropriate features after clustering and showed that the accuracy of the feature extraction greatly improved. Sophie et al (2018) focused on semantic enrichment by employing ontology features in determining the sentiment value of a given pair of review and feature. Chen et al (2018) designed a text analytics framework to assess secondhand sellers' reputations and developed a feature extraction method that combined the results of domain ontology and topic modeling to extract topical features.…”

Section: Product Review Domain Ontologymentioning

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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“…An an aspect level classification consists of the representation of sentiments as triplets {user, feature, sentiment}. In this sense, ontologies have been proved to be an effective method to extract the aspects for an specific domain [11][12][13][14][15][16].…”

Section: Opinion Miningmentioning

confidence: 99%

Evaluating Information-Retrieval Models and Machine-Learning Classifiers for Measuring the Social Perception towards Infectious Diseases

et al. 2019

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Recent outbreaks of infectious diseases remind us the importance of early-detection systems improvement. Infodemiology is a novel research field that analyzes online information regarding public health that aims to complement traditional surveillance methods. However, the large volume of information requires the development of algorithms that handle natural language efficiently. In the bibliography, it is possible to find different techniques to carry out these infodemiology studies. However, as far as our knowledge, there are no comprehensive studies that compare the accuracy of these techniques. Consequently, we conducted an infodemiology-based study to extract positive or negative utterances related to infectious diseases so that future syndromic surveillance systems can be improved. The contribution of this paper is two-fold. On the one hand, we use Twitter to compile and label a balanced corpus of infectious diseases with 6164 utterances written in Spanish and collected from Central America. On the other hand, we compare two statistical-models: word-grams and char-grams. The experimentation involved the analysis of different gram sizes, different partitions of the corpus, and two machine-learning classifiers: Random-Forest and Sequential Minimal Optimization. The results reach a 90.80% of accuracy applying the char-grams model with five-char-gram sequences. As a final contribution, the compiled corpus is released.

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Review-aggregated aspect-based sentiment analysis with ontology features

Cited by 25 publications

References 18 publications

ABSA: Computational Measurement Analysis Approach for Prognosticated Aspect Extraction System

ABSA: Computational Measurement Analysis Approach for Prognosticated Aspect Extraction System

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

Evaluating Information-Retrieval Models and Machine-Learning Classifiers for Measuring the Social Perception towards Infectious Diseases

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