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| 1 | Aspect Extraction Approach for Sentiment Analysis Using Keywords显示文摘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. | Nafees Ayub Muhammad Ramzan Talib Muhammad Kashif Hanif Muhammad Awais | 2023 | Computers, Materials & Continua2023，，3： | 0 |
| 2 | Deep Learning and Machine Learning-Based Model for Conversational Sentiment Classification显示文摘In the current era of the internet,people use online media for conversation,discussion,chatting,and other similar purposes.Analysis of such material where more than one person is involved has a spate challenge as compared to other text analysis tasks.There are several approaches to identify users’emotions fromthe conversational text for the English language,however regional or low resource languages have been neglected.The Urdu language is one of them and despite being used by millions of users across the globe,with the best of our knowledge there exists no work on dialogue analysis in the Urdu language.Therefore,in this paper,we have proposed a model which utilizes deep learning and machine learning approaches for the classification of users’emotions from the text.To accomplish this task,we have first created a dataset for the Urdu language with the help of existing English language datasets for dialogue analysis.After that,we have preprocessed the data and selected dialogues with common emotions.Once the dataset is prepared,we have used different deep learning and machine learning techniques for the classification of emotion.We have tuned the algorithms according to the Urdu language datasets.The experimental evaluation has shown encouraging results with 67%accuracy for the Urdu dialogue datasets,more than 10,000 dialogues are classified into five emotions i.e.,joy,fear,anger,sadness,and neutral.We believe that this is the first effort for emotion detection from the conversational text in the Urdu language domain. | Sami Ullah Muhammad Ramzan Talib Toqir A.Rana Muhammad Kashif Hanif Muhammad Awais | 2022 | Computers, Materials & Continua2022，，8： | 0 |
| 3 | Google Scholar University Ranking Algorithm to Evaluate the Quality of Institutional Research显示文摘Education quality has undoubtedly become an important local and international benchmark for education,and an institute’s ranking is assessed based on the quality of education,research projects,theses,and dissertations,which has always been controversial.Hence,this research paper is influenced by the institutes ranking all over the world.The data of institutes are obtained through Google Scholar(GS),as input to investigate the United Kingdom’s Research Excellence Framework(UK-REF)process.For this purpose,the current research used a Bespoke Program to evaluate the institutes’ranking based on their source.The bespoke program requires changes to improve the results by addressing these methodological issues:Firstly,Redundant profiles,which increased their citation and rank to produce false results.Secondly,the exclusion of theses and dissertation documents to retrieve the actual publications to count for citations.Thirdly,the elimination of falsely owned articles from scholars’profiles.To accomplish this task,the experimental design referred to collecting data from 120 UK-REF institutes and GS for the present year to enhance its correlation analysis in this new evaluation.The data extracted from GS is processed into structured data,and afterward,it is utilized to generate statistical computations of citations’analysis that contribute to the ranking based on their citations.The research promoted the predictive approach of correlational research.Furthermore,experimental evaluation reported encouraging results in comparison to the previous modi-fication made by the proposed taxonomy.This paper discussed the limitations of the current evaluation and suggested the potential paths to improve the research impact algorithm. | Noor Ul Sabah Muhammad Murad Khan Ramzan Talib Muhammad Anwar Muhammad Sheraz Arshad Malik Puteri Nor Ellyza Nohuddin | 2023 | Computers, Materials & Continua2023，，6： | 0 |
| 4 | Contextual Text Mining Framework for Unstructured Textual Judicial Corpora through Ontologies显示文摘Digitalization has changed the way of information processing, and newtechniques of legal data processing are evolving. Text mining helps to analyze andsearch different court cases available in the form of digital text documents toextract case reasoning and related data. This sort of case processing helps professionals and researchers to refer the previous case with more accuracy in reducedtime. The rapid development of judicial ontologies seems to deliver interestingproblem solving to legal knowledge formalization. Mining context informationthrough ontologies from corpora is a challenging and interesting field. Thisresearch paper presents a three tier contextual text mining framework throughontologies for judicial corpora. This framework comprises on the judicial corpus,text mining processing resources and ontologies for mining contextual text fromcorpora to make text and data mining more reliable and fast. A top-down ontologyconstruction approach has been adopted in this paper. The judicial corpus hasbeen selected with a sufficient dataset to process and evaluate the results.The experimental results and evaluations show significant improvements incomparison with the available techniques. | Zubair Nabi Ramzan Talib Muhammad Kashif Hanif Muhammad Awais | 2022 | Computer Systems Science & Engineering2022，43，12： | 0 |
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