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Multi Layered Rule-Based Technique for Explicit aspect extraction from Online Reviews

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Computers, Materials & Continua 2022年第12期73卷 4641-4656页

作者： Mubashar Hussain Toqir A.Rana Aksam Iftikhar M.Usman Ashraf Muhammad Waseem Iqbal Ahmed Alshaflut Abdullah Alourani School of Systems and Technology University of Management and TechnologyLahore54770Pakistan Department of Computer Science and It The University of LahoreLahore54000Pakistan School of Computer Sciences Universiti Sains Malaysia(USM)Penang11800Malaysia Department of Computer Science Comsats University IslamabadLahore54000Pakistan Department of Computer Science GC Women University SialkotPakistan Department of Software Engineering The Superior UniversityLahore53700Pakistan Faculty of Computing and It Albaha UniversityAlbahaSaudi Arabia Department of Computer Science Majmaah UniversitySaudi Arabia

In the field of sentiment analysis,extracting aspects or opinion targets fromuser reviews about a product is a key task.Extracting the polarity of an opinion is much more useful if we also know the targeted aspect or Feature.Rule based approaches,like dependency-based rules,are quite popular and effective for this purpose.However,they are heavily dependent on the authenticity of the employed parts-of-speech(POS)tagger and dependency parser.Another popular rule based approach is to use sequential rules,wherein the rules formulated by learning from the user’s behavior.However,in general,the sequential rule-based approaches have poor generalization capability.Moreover,existing approaches mostly consider an aspect as a noun or noun phrase,so these approaches are unable to extract verb aspects.In this article,we have proposed a multi-layered rule-based(ML-RB)technique using the syntactic dependency parser based rules along with some selective sequential rules in separate layers to extract noun aspects.Additionally,after rigorous analysis,we have also constructed rules for the extraction of verb aspects.These verb rules primarily based on the association between verb and opinion words.The proposed multi-layer technique compensates for the weaknesses of individual layers and yields improved results on two publicly available customer review datasets.The F1 score for both the datasets are 0.90 and 0.88,respectively,which are better than existing approaches.These improved results can be attributed to the application of sequential/syntactic rules in a layered manner as well as the capability to extract both noun and verb aspects.

关键词： Explicit aspect aspect extraction opinion mining rule-based verb aspects

A Model for Cross-Domain Opinion Target extraction in Sentiment Analysis

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Computer Systems Science & Engineering 2022年第9期42卷 1215-1239页

作者： Muhammet Yasin PAK Serkan GUNAL Department of Computer Engineering Eskisehir Technical UniversityEskisehirTurkiye

Opinion target extraction is one of the core tasks in sentiment analysison text data. In recent years, dependency parser–based approaches have beencommonly studied for opinion target extraction. However, dependency parsersare limited by language and grammatical constraints. Therefore, in this work, asequential pattern-based rule mining model, which does not have such constraints,is proposed for cross-domain opinion target extraction from product reviews inunknown domains. Thus, knowing the domain of reviews while extracting opinion targets becomes no longer a requirement. The proposed model also revealsthe difference between the concepts of opinion target and aspect, which are commonly confused in the literature. The model consists of two stages. In the firststage, the aspects of reviews are extracted from the target domain using the rulesautomatically generated from source domains. The aspects are also transferredfrom the source domains to a target domain. Moreover, aspect pruning is appliedto further improve the performance of aspect extraction. In the second stage, theopinion target is extracted among the aspects extracted at the former stage usingthe rules automatically generated for opinion target extraction. The proposedmodel was evaluated on several benchmark datasets in different domains andcompared against the literature. The experimental results revealed that the opiniontargets of the reviews in unknown domains can be extracted with higher accuracythan those of the previous works.

关键词： Opinion target extraction aspect extraction sentiment analysis

aspect-Based Sentiment Classification Using Deep Learning and Hybrid of Word Embedding and Contextual Position

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Intelligent Automation & Soft Computing 2023年第9期37卷 3101-3124页

作者： Waqas Ahmad Hikmat Ullah Khan Fawaz Khaled Alarfaj Saqib Iqbal Abdullah Mohammad Alomair Naif Almusallam Department of Computer Science COMSATS University IslamabadWah CampusWah47040Pakistan Department of Computer Science Namal UniversityMianwali42201Pakistan Department of Management Information Systems(MIS) College of Business AdministrationKing Faisal University(KFU)Al-Ahsa31982Saudi Arabia Department of Software Engineering Al Ain UniversityAl Ain64141United Arab Emirates

aspect-based sentiment analysis aims to detect and classify the sentiment polarities as negative,positive,or neutral while associating them with their identified aspects from the corresponding context.In this regard,prior methodologies widely utilize either word embedding or tree-based rep-resentations.Meanwhile,the separate use of those deep features such as word embedding and tree-based dependencies has become a significant cause of information loss.Generally,word embedding preserves the syntactic and semantic relations between a couple of terms lying in a sentence.Besides,the tree-based structure conserves the grammatical and logical dependencies of context.In addition,the sentence-oriented word position describes a critical factor that influences the contextual information of a targeted sentence.Therefore,knowledge of the position-oriented information of words in a sentence has been considered significant.In this study,we propose to use word embedding,tree-based representation,and contextual position information in combination to evaluate whether their combination will improve the result’s effectiveness or not.In the meantime,their joint utilization enhances the accurate identification and extraction of targeted aspect terms,which also influences their classification process.In this research paper,we propose a method named Attention Based Multi-Channel Convolutional Neural Net-work(Att-MC-CNN)that jointly utilizes these three deep features such as word embedding with tree-based structure and contextual position informa-tion.These three parameters deliver to Multi-Channel Convolutional Neural Network(MC-CNN)that identifies and extracts the potential terms and classifies their polarities.In addition,these terms have been further filtered with the attention mechanism,which determines the most significant words.The empirical analysis proves the proposed approach’s effectiveness compared to existing techniques when evaluated on standard datasets.The experimental results represent our approach outperforms in the F1

关键词： Sentiment analysis word embedding aspect extraction consistency tree multichannel convolutional neural network contextual position information

aspect-based sentiment analysis of online peer reviews and prediction of paper acceptance results

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Data Science and Informetrics 2023年第1期3卷 37-64页

作者： Minghui Meng Ruxue Han Jiangtao Zhong Haomin Zhou Chengzhi Zhang Department of Information Management Nanjing University of Science and TechnologyNanjingChina Key Laboratory of Rich-media Knowledge Organization and Service of Digital Publishing Content Institute of Scientific&Technical Information of China BeijingChina

Peer reviews of academic articles contain reviewers’ overall impressions and specific comments on the contributed articles,which have a lot of sentimental information.By exploring the fine-grained sentiments in peer reviews,we can discover critical aspects of interest to the reviewers.The results can also assist editors and chairmen in making final decisions.However,current research on the aspects of peer reviews is coarse-grained,and mostly focuses on the overall evaluation of the review objects.Therefore,this paper constructs a multi-level fine-grained aspect set of peer reviews for further study.First,this paper uses the multi-level aspect extraction method to extract the aspects from peer reviews of ICLR conference papers.Comparative experiments confirm the validity of the method.Secondly,various Deep Learning models are used to classify aspects’ sentiments automatically,with LCFS-BERT performing best.By calculating the correlation between sentimental scores of the review aspects and the acceptance result of papers,we can find the important aspects affecting acceptance.Finally,this paper predicts acceptance results of papers(accepted/rejected) according to the peer reviews.The optimal acceptance prediction model is XGboost,achieving a Macro_F1 score of 87.43%.

关键词： Peer reviews aspect extraction Sentiment analysis Prediction of paper acceptance results

Ensemble Based Temporal Weighting and Pareto Ranking (ETP) Model for Effective Root Cause Analysis

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Computers, Materials & Continua 2021年第10期69卷 819-830页

作者： Naveen Kumar Seerangan S.Vijayaragavan Shanmugam Department of Computer Science(Category-B) Bharathiar UniversityCoimbatore641046Tamil NaduIndia CSE Muthayammal Engineering CollegeRasipuram637408Tamil NaduIndia

Root-cause identification plays a vital role in business decision making by providing effective future directions for the organizations.aspect extraction and sentiment extraction plays a vital role in identifying the rootcauses.This paper proposes the Ensemble based temporal weighting and pareto ranking(ETP)model for Root-cause identification.aspect extraction is performed based on rules and is followed by opinion identification using the proposed boosted ensemble model.The obtained aspects are validated and ranked using the proposed aspect weighing scheme.Pareto-rule based aspect selection is performed as the final selection mechanism and the results are presented for business decision making.Experiments were performed with the standard five product benchmark dataset.Performances on all five product reviews indicate the effective performance of the proposed model.Comparisons are performed using three standard state-of-the-art models and effectiveness is measured in terms of F-Measure and Detection rates.The results indicate improved performances exhibited by the proposed model with an increase in F-Measure levels at 1%–15%and detection rates at 4%–24%compared to the state-of-the-art models.

关键词： Root cause analysis sentiment analysis aspect extraction ensemble modelling temporal weighting pareto ranking