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sentiment analysis and Classification Using Deep Semantic Information and Contextual Knowledge

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Computers, Materials & Continua 2023年第1期74卷 671-691页

作者： Ahmed Abdulhakim Al-Absi Dae-Ki Kang Mohammed Abdulhakim Al-Absi Department of Smart Computing Kyungdong University 464-gilBongpoGosungGangwon-do24764Korea Division of Computer Engineering Dongseo University47 Jurye-roSasang-guBusan47011Korea Department of Computer Engineering Dongseo University47 Jurye-roSasang-guBusan47011Korea

sentiment analysis(AS)is one of the basic research directions in natural language processing(NLP),it is widely adopted for news,product review,and ***-based sentiment analysis(ABSA)aims at identifying the sentiment polarity of a given target context,previous existing model of sentiment analysis possesses the issue of the insufficient exaction of features which results in low *** this research work develops a deep-semantic and contextual knowledge networks(DSCNet).DSCNet tends to exploit the semantic and contextual knowledge to understand the context and enhance the accuracy based on given *** first temporal relationships are established then deep semantic knowledge and contextual knowledge are ***,a deep integration layer is introduced to measure the importance of features for efficient extraction of different *** of DSCNet model lies in introducing the deep *** is evaluated on three datasets i.e.,Restaurant,Laptop,and Twitter dataset considering different deep learning(DL)metrics like precision,recall,accuracy,and Macro-F1 ***,comparative analysis is carried out with different baselinemethods in terms of accuracy andMacro-F1 *** achieves 92.59%of accuracy on restaurant dataset,86.99%of accuracy on laptop dataset and 78.76%of accuracy on Twitter dataset.

关键词： sentiment analysis aspect-sa deep learning DSCNet

sentiment analysis of Short Texts Based on Parallel DenseNet

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Computers, Materials & Continua 2021年第10期69卷 51-65页

作者： Luqi Yan Jin Han Yishi Yue Liu Zhang Yannan Qian Nanjing University of Information Science&Technology Nanjing210044China State Grid Hunan Electric Power Company Limited Research Institute Changsha410007China Waterford Institute of Technology WaterfordX91 K0EKIreland

Text sentiment analysis is a common problem in the field of natural language processing that is often resolved by using convolutional neural networks(CNNs).However,most of these CNN models focus only on learning local features while ignoring global *** this paper,based on traditional densely connected convolutional networks(DenseNet),a parallel DenseNet is proposed to realize sentiment analysis of short ***,this paper proposes two novel feature extraction blocks that are based on DenseNet and a multiscale convolutional neural ***,this paper solves the problem of ignoring global features in traditional CNN models by combining the original features with features extracted by the parallel feature extraction block,and then sending the combined features into the final ***,a model based on parallel DenseNet that is capable of simultaneously learning both local and global features of short texts and shows better performance on six different databases compared to other basic models is proposed.

关键词： sentiment analysis short texts parallel DenseNet

sentiment analysis of Roman Urdu on E-Commerce Reviews Using Machine Learning

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Computer Modeling in Engineering & Sciences 2022年第6期131卷 1263-1287页

作者： Bilal Chandio Asadullah Shaikh Maheen Bakhtyar Mesfer Alrizq Junaid Baber Adel Sulaiman Adel Rajab Waheed Noor Department of Computer Science and Information Technology University of BalochistanQuetta87300Pakistan College of Computer Science and Information Systems Najran UniversityNajran61441Saudi Arabia Department of Information Technology University of BalochistanQuetta87300Pakistan

sentiment analysis task has widely been studied for various languages such as English and ***,Roman Urdu sentiment analysis yet requires more attention from peer-researchers due to the lack of Off-the-Shelf Natural Language Processing(NLP)*** primary objective of this study is to investigate the diverse machine learning methods for the sentiment analysis of Roman Urdu data which is very informal in nature and needs to be lexically *** mitigate this challenge,we propose a fine-tuned Support Vector Machine(SVM)powered by Roman Urdu *** our proposed scheme,the corpus data is initially cleaned to remove the anomalies from the *** initial pre-processing,each user review is being *** input text is transformed into a feature vector using the bag-of-word ***,the SVM is used to classify and detect user *** proposed scheme is based on a dictionary based Roman Urdu *** creation of the Roman Urdu stemmer is aimed at standardizing the text so as to minimize the level of *** efficacy of our proposed model is also empirically evaluated with diverse experimental configurations,so as to fine-tune the hyper-parameters and achieve superior ***,a series of experiments are conducted on diverse machine learning and deep learning models to compare the performance with our proposed *** also introduced the largest dataset on Roman Urdu,i.e.,Roman Urdu e-commerce dataset(RUECD),which contains 26K+user reviews annotated by the group of *** RUECD is challenging and the largest dataset available of Roman *** experiments show that the newly generated dataset is quite challenging and requires more attention from the peer researchers for Roman Urdu sentiment analysis.

关键词： sentiment analysis Roman Urdu machine learning SVM

sentiment analysis on Social Media Using Genetic Algorithm with CNN

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Computers, Materials & Continua 2022年第3期70卷 5399-5419页

作者： Dharmendra Dangi Amit Bhagat Dheeraj Kumar Dixit Department of Mathematics and Computer Applications Maulana Azad National Institute of Technology(MANIT)BhopalMadhya PradeshIndia

There are various intense forces causing customers to use evaluated data when using social media platforms and microblogging ***,customers throughout the world share their points of view on all kinds of topics through these *** massive volume of data created by these customers makes it impossible to analyze such data ***,an efficient and intelligent method for evaluating social media data and their divergence needs to be ***,various types of equipment and techniques are available for automatically estimating the classification of *** analysis involves determining people’s emotions using facial *** analysis can be performed for any individual based on specific *** present study describes the analysis of an image dataset using CNNswithPCA intended to detect people’s sentiments(specifically,whether a person is happy or sad).This process is optimized using a genetic algorithm to get better ***,a comparative analysis has been conducted between the different models generated by changing the mutation factor,performing batch normalization,and applying feature reduction using *** steps are carried out across five experiments using *** maximum accuracy obtained is 96.984%,which is associated with the Happy and Sad sentiments.

关键词： sentiment analysis convolutional neural networks facial expression genetic algorithm

sentiment analysis Using E-Commerce Review Keyword-Generated Image with a Hybrid Machine Learning-Based Model

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Computers, Materials & Continua 2024年第7期80卷 1581-1599页

作者： Jiawen Li Yuesheng Huang Yayi Lu Leijun Wang Yongqi Ren Rongjun Chen School of Computer Science Guangdong Polytechnic Normal UniversityGuangzhou510665China Hubei Province Key Laboratory of Occupational Hazard Identification and Control Wuhan University of Science and TechnologyWuhan430065China

In the context of the accelerated pace of daily life and the development of e-commerce,online shopping is a mainstreamway for consumers to access products and *** understand their emotional expressions in facing different shopping experience scenarios,this paper presents a sentiment analysis method that combines the ecommerce reviewkeyword-generated imagewith a hybrid machine learning-basedmodel,inwhich theWord2Vec-TextRank is used to extract keywords that act as the inputs for generating the related images by generative Artificial Intelligence(AI).Subsequently,a hybrid Convolutional Neural Network and Support Vector Machine(CNNSVM)model is applied for sentiment classification of those keyword-generated *** method validation,the data randomly comprised of 5000 reviews from Amazon have been *** superior keyword extraction capability,the proposedmethod achieves impressive results on sentiment classification with a remarkable accuracy of up to 97.13%.Such performance demonstrates its advantages by using the text-to-image approach,providing a unique perspective for sentiment analysis in the e-commerce review data compared to the existing ***,the proposed method enhances the reliability and insights of customer feedback surveys,which would also establish a novel direction in similar cases,such as social media monitoring and market trend research.

关键词： sentiment analysis keyword-generated image machine learning Word2Vec-TextRank CNN-SVM

sentiment analysis in Social Media for Competitive Environment Using Content analysis

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Computers, Materials & Continua 2022年第6期71卷 5603-5618页

作者： Shahid Mehmood Imran Ahmad Muhammad Adnan Khan Faheem Khan T.Whangbo Riphah School of Computing&Innovation Faculty of ComputingRiphah International University Lahore CampusLahore54000Pakistan Pattern Recognition and Machine Learning Lab Department of SoftwareGachon UniversitySeongnamGyeonggido13120Korea Department of Computer Engineering Gachon UniversitySeongnam13557Korea

Education sector has witnessed several changes in the recent *** changes have forced private universities into fierce competition with each other to get more students *** competition has resulted in the adoption of marketing practices by private universities similar to commercial *** get competitive gain,universities must observe and examine the students’feedback on their own social media sites along with the social media sites of their *** study presents a novel framework which integrates numerous analytical approaches including statistical analysis,sentiment analysis,and text mining to accomplish a competitive analysis of social media sites of the *** techniques enable local universities to utilize social media for the identification of the most-discussed topics by students as well as based on the most unfavorable comments received,major areas for improvement.A comprehensive case study was conducted utilizing the proposed framework for competitive analysis of few top ranked international universities as well as local private universities in Lahore *** results show that diversity of shared content,frequency of posts,and schedule of updates,are the key areas for improvement for the local *** on the competitive intelligence gained several recommendations are included in this paper that would enable local universities generally and Riphah international university(RIU)Lahore specifically to promote their brand and increase their attractiveness for potential students using social media and launch successful marketing campaigns targeting a large number of audiences at significantly reduced cost resulting in an increased number of enrolments.

关键词： Social media higher education sentiment analysis content analysis competitive analysis text mining

sentiment analysis for Chinese Text Based on Emotion Degree Lexicon and Cognitive Theories

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Journal of Shanghai Jiaotong university(Science) 2015年第1期20卷 1-6页

作者：武星吕海涛卓少剑 School of Computer Engineering and Science Shanghai University

The mass data of social media and social networks generated by users play an important role in tracking users’sentiments and opinions online.A good polarity lexicon which can effectively improve the classification results of sentiment analysis is indispensable to analyze the user’s *** by social cognitive theories,we combine basic emotion value lexicon and social evidence lexicon to improve traditional polarity *** proposed method obtains significant improvement in Chinese text sentiment analysis by using the proposed lexicon and new syntactic analysis method.

关键词： Chinese text sentiment analysis emotion lexicon social cognitive theory emotion tendency

sentiment analysis with Tweets Behaviour in Twitter Streaming API

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Computer Systems Science & Engineering 2023年第5期45卷 1113-1128页

作者： Kuldeep Chouhan Mukesh Yadav Ranjeet Kumar Rout Kshira Sagar Sahoo NZ Jhanjhi Mehedi Masud Sultan Aljahdali Computer Science and Engineering I.T.S Engineering CollegeGreater Noida201310India Computer Science and Engineering DPG Institute of Technology and ManagementGurgaon122004India Computer Science and Engineering National Institute of Technology SrinagarJammu and KashmirIndia Department of Computer Science and Engineering SRM UniversityAmaravatiAndhra Pradesh522240India School of Computer Science SCS Taylor’s UniversitySubang Jaya47500Malaysia Department of Computer Science College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif21944Saudi Arabia

Twitter is a radiant platform with a quick and effective technique to analyze users’perceptions of activities on social *** researchers and industry experts show their attention to Twitter sentiment analysis to recognize the stakeholder *** sentiment analysis needs an advanced level of approaches including adoption to encompass data sentiment analysis and various machine learning *** assessment of sentiment analysis in multiple fields that affect their elevations among the people in real-time by using Naive Bayes and Support Vector Machine(SVM).This paper focused on analysing the distinguished sentiment techniques in tweets behaviour datasets for various spheres such as healthcare,behaviour estimation,*** addition,the results in this work explore and validate the statistical machine learning classifiers that provide the accuracy percentages attained in terms of positive,negative and neutral *** this work,we obligated Twitter Application Programming Interface(API)account and programmed in python for sentiment analysis approach for the computational measure of user’s perceptions that extract a massive number of tweets and provide market value to the Twitter account *** distinguish the results in terms of the performance evaluation,an error analysis investigates the features of various stakeholders comprising social media analytics researchers,Natural Language Processing(NLP)developers,engineering managers and experts involved to have a decision-making approach.

关键词： Machine learning Naive Bayes natural language processing sentiment analysis social media analytics support vector machine Twitter application programming interface

Structural information aware deep semi-supervised recurrent neural network for sentiment analysis

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Frontiers of Computer Science 2015年第2期9卷 171-184页

作者： Wenge RONG Baolin PENG Yuanxin OUYANG Chao LI Zhang XIONG School of Computer Science and Engineering Beihang University Beijing 100191 China Research Institute of Beihang University in Shenzhen Shenzhen 518057 China

With the development of Internet, people are more likely to post and propagate opinions online. sentiment analysis is then becoming an important challenge to under- stand the polarity beneath these comments. Currently a lot of approaches from natural language processing＇s perspec- tive have been employed to conduct this task. The widely used ones include bag-of-words and semantic oriented analy- sis methods. In this research, we further investigate the struc- tural information among words, phrases and sentences within the comments to conduct the sentiment analysis. The idea is inspired by the fact that the structural information is play- ing important role in identifying the overall statement＇s po- larity. As a result a novel sentiment analysis model is pro- posed based on recurrent neural network, which takes the par- tial document as input and then the next parts to predict the sentiment label distribution rather than the next word. The proposed method learns words representation simultaneously the sentiment distribution. Experimental studies have been conducted on commonly used datasets and the results have shown its promising potential.

关键词： sentiment analysis recurrent neural network,deep learning machine learning