Aspect ontology based review exploration

Konjengbam, Anand; Dewangan, Neelesh; Kumar, Nagendra; Singh, Manish

doi:10.1016/j.elerap.2018.05.006

Cited by 16 publications

(7 citation statements)

References 14 publications

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“…The heuristic pattern is used in Asghar et al 39 for aspect extraction in English texts. Summarizing the users' opinions is performed in Konjengbam et al 40 using ontology relationships among different aspects of a product and considering an aspect of a product as a subset of another aspect. When searching for an aspect of a product, all opinions related to the aspect and its subsets are presented.…”

Section: Related Workmentioning

confidence: 99%

Deep aspect extraction and classification for opinion mining in e‐commerce applications using convolutional neural network feature extraction followed by long short term memory attention model

Sharbatian,

Moattar

2023

Applied AI Letters

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Users of e‐commerce websites review different aspects of a product in the comment section. In this research, an approach is proposed for opinion aspect extraction and recognition in selling systems. We have used the users' opinions from the Digikala website (www.Digikala.com), which is an Iranian e‐commerce company. In this research, a language‐independent framework is proposed that is adjustable to other languages. In this regard, after necessary text processing and preparation steps, the existence of an aspect in an opinion is determined using deep learning algorithms. The proposed model combines Convolutional Neural Network (CNN) and long‐short‐term memory (LSTM) deep learning approaches. CNN is one of the best algorithms for extracting latent features from data. On the other hand, LSTM can detect latent temporal relationships among different words in a text due to its memory ability and attention model. The approach is evaluated on six classes of opinion aspects. Based on the experiments, the proposed model's accuracy, precision, and recall are 70%, 60%, and 85%, respectively. The proposed model was compared in terms of the above criteria with CNN, Naive Bayes, and SVM algorithms and showed satisfying performance.

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Section: Related Workmentioning

confidence: 99%

Deep aspect extraction and classification for opinion mining in e‐commerce applications using convolutional neural network feature extraction followed by long short term memory attention model

Sharbatian,

Moattar

2023

Applied AI Letters

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“…The second sub-task, which is known as sentiment classification, consists of assigning a sentiment to each aspect. As the most important indicators of sentiments are opinion words, such as good, bad, poor, or terrible, some researchers have applied sentiment lexicons in order to determine the subjective polarity of each word in context [24]. However, sentiment lexicons are not sufficient owing to the complex phenomena present in natural language, such as the usage of figurative language, which changes the meaning of an utterance from its literal meaning [25].…”

Section: Aspect-based Sentiment Analysis Classificationmentioning

confidence: 99%

Ontology-driven aspect-based sentiment analysis classification: An infodemiological case study regarding infectious diseases in Latin America

García-Díaz

Cánovas-García

Valencia‐García

2020

Future Generation Computer Systems

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Infodemiology is the process of mining unstructured and textual data so as to provide public health officials and policymakers with valuable information regarding public health. The appearance of this new data source, which was previously unimaginable, has opened up a new way in which to improve public health systems, resulting in better communication policies and better detection systems. However, the unstructured nature of the Internet, along with the complexity of the infectious disease domain, prevents the information extracted from being easily understood. Moreover, when dealing with languages other than English, for which some of the most common Natural Language Processing resources are not available, the correct exploitation of this data becomes even more difficult. We intend to fill these gaps proposing an ontology-driven aspect-based sentiment analysis with which to measure the general public's opinions as regards infectious diseases when expressed in Spanish by employing a case study of tweets concerning the Zika, Dengue and Chikungunya viruses in Latin America. Our proposal is based on two technologies. We first use ontologies in order to model the infectious disease domain with concepts such as risks, symptoms, transmission methods or drugs, among other concepts. We then measure the relationship between these concepts in order to determine the degree to which one concept influences other concepts. This new information is subsequently applied in order to build an aspect-based sentiment analysis model based on statistical and linguistic features. This is done by applying deep-learning models. Our proposal is available on a web platform, where users can see the sentiment for each concept at a glance and analyse how each concept influences the sentiment of the others.

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“…When given a review without any explicit aspects, the system could select the cluster with the highest frequency weight and choose one representative word as the implicit aspect of the review. Likewise, Konjengbam [56] employs POS-tags and dependency tree to mine the frequent nouns as aspect terms. Based on the extracted aspect terms, Konjengbam also devises an ontology based opinion summarization method in [56].…”

Section: ) Rule-based Aspect Extractionmentioning

confidence: 99%

“…Likewise, Konjengbam [56] employs POS-tags and dependency tree to mine the frequent nouns as aspect terms. Based on the extracted aspect terms, Konjengbam also devises an ontology based opinion summarization method in [56].…”

Section: ) Rule-based Aspect Extractionmentioning

confidence: 99%

A Survey on Opinion Mining: From Stance to Product Aspect

Wang¹,

Zhou²,

Jiang³

et al. 2019

IEEE Access

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With the prevalence of social media and online forum, opinion mining, aiming at analyzing and discovering the latent opinion in user-generated reviews on the Internet, has become a hot research topic. This survey focuses on two important subtasks in this field, stance detection and product aspect mining, both of which can be formalized as the problem of the triple target, aspect, opinion extraction. In this paper, we first introduce the general framework of opinion mining and describe the evaluation metrics. Then, the methodologies for stance detection on different sources, such as online forum and social media are discussed. After that, approaches for product aspect mining are categorized into three main groups which are corpus level aspect extraction, corpus level aspect, and opinion mining, and document level aspect and opinion mining based on the processing units and tasks. And then we discuss the challenges and possible solutions. Finally, we summarize the evolving trend of the reviewed methodologies and conclude the survey. INDEX TERMS Opinion mining, stance detection, product aspect mining, topic model, deep neural network.

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Aspect ontology based review exploration

Cited by 16 publications

References 14 publications

Deep aspect extraction and classification for opinion mining in e‐commerce applications using convolutional neural network feature extraction followed by long short term memory attention model

Deep aspect extraction and classification for opinion mining in e‐commerce applications using convolutional neural network feature extraction followed by long short term memory attention model

Ontology-driven aspect-based sentiment analysis classification: An infodemiological case study regarding infectious diseases in Latin America

A Survey on Opinion Mining: From Stance to Product Aspect

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