Aspect Extraction Approach for Sentiment Analysis Using Keywords

Sentiment Analysis deals with consumer reviews available on blogs,discussion forums,E-commerce websites,andApp Store.These online reviews about products are also becoming essential for consumers and companies as well.Consumers rely on these reviews to make their decisions about products and companies are also very interested in these reviews to judge their products and services.These reviews are also a very precious source of information for requirement engineers.But companies and consumers are not very satisfied with the overall sentiment;they like fine-grained knowledge about consumer reviews.Owing to this,many researchers have developed approaches for aspect-based sentiment analysis.Most existing approaches concentrate on explicit aspects to analyze the sentiment,and only a few studies rely on capturing implicit aspects.This paper proposes a Keywords-Based Aspect Extraction method,which captures both explicit and implicit aspects.It also captures opinion words and classifies the sentiment about each aspect.We applied semantic similarity-basedWordNet and SentiWordNet lexicon to improve aspect extraction.We used different collections of customer reviews for experiment purposes,consisting of eight datasets over seven domains.We compared our approach with other state-of-the-art approaches,including Rule Selection using Greedy Algorithm(RSG),Conditional Random Fields(CRF),Rule-based Extraction(RubE),and Double Propagation(DP).Our results have shown better performance than all of these approaches.

Multi Layered Rule-Based Technique for Explicit Aspect Extraction from Online Reviews

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.

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

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.

Multi-Task Learning Model with Data Augmentation for Arabic Aspect-Based Sentiment Analysis

Aspect-based sentiment analysis(ABSA)is a fine-grained process.Its fundamental subtasks are aspect termextraction(ATE)and aspect polarity classification(APC),and these subtasks are dependent and closely related.However,most existing works on Arabic ABSA content separately address them,assume that aspect terms are preidentified,or use a pipeline model.Pipeline solutions design different models for each task,and the output from the ATE model is used as the input to the APC model,which may result in error propagation among different steps because APC is affected by ATE error.These methods are impractical for real-world scenarios where the ATE task is the base task for APC,and its result impacts the accuracy of APC.Thus,in this study,we focused on a multi-task learning model for Arabic ATE and APC in which the model is jointly trained on two subtasks simultaneously in a singlemodel.This paper integrates themulti-task model,namely Local Cotext Foucse-Aspect Term Extraction and Polarity classification(LCF-ATEPC)and Arabic Bidirectional Encoder Representation from Transformers(AraBERT)as a shred layer for Arabic contextual text representation.The LCF-ATEPC model is based on a multi-head selfattention and local context focus mechanism(LCF)to capture the interactive information between an aspect and its context.Moreover,data augmentation techniques are proposed based on state-of-the-art augmentation techniques(word embedding substitution with constraints and contextual embedding(AraBERT))to increase the diversity of the training dataset.This paper examined the effect of data augmentation on the multi-task model for Arabic ABSA.Extensive experiments were conducted on the original and combined datasets(merging the original and augmented datasets).Experimental results demonstrate that the proposed Multi-task model outperformed existing APC techniques.Superior results were obtained by AraBERT and LCF-ATEPC with fusion layer(AR-LCF-ATEPC-Fusion)and the proposed data augmentation word embedding-based method(FastText)on the combined dataset.

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

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 measure with an overall achievement of 94%in identifying aspects and 92%in the task of sentiment classification.

End-to-end aspect category sentiment analysis based on type graph convolutional networks

For the existing aspect category sentiment analysis research,most of the aspects are given for sentiment extraction,and this pipeline method is prone to error accumulation,and the use of graph convolutional neural network for aspect category sentiment analysis does not fully utilize the dependency type information between words,so it cannot enhance feature extraction.This paper proposes an end-to-end aspect category sentiment analysis(ETESA)model based on type graph convolutional networks.The model uses the bidirectional encoder representation from transformers(BERT)pretraining model to obtain aspect categories and word vectors containing contextual dynamic semantic information,which can solve the problem of polysemy;when using graph convolutional network(GCN)for feature extraction,the fusion operation of word vectors and initialization tensor of dependency types can obtain the importance values of different dependency types and enhance the text feature representation;by transforming aspect category and sentiment pair extraction into multiple single-label classification problems,aspect category and sentiment can be extracted simultaneously in an end-to-end way and solve the problem of error accumulation.Experiments are tested on three public datasets,and the results show that the ETESA model can achieve higher Precision,Recall and F1 value,proving the effectiveness of the model.

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

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.

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

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%.