[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124167-en":3,"doc-seo-124167-105":29,"detail-sidebar-cat-0-en-105":90},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":11,"language":21,"language_code":22,"site_id":23,"html_lang":22,"table_of_contents":24,"faqs":25,"seo_title":26,"seo_description":14,"update_tm":27,"read_time":28},124167,13056703019404,"Miles","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Improving sentiment analysis using text network features within different machine learning algorithms","Sentiment analysis remains difficult because natural language is subjective and social platforms contain many unstandardized dialects. Existing studies often overlook network representation learning for sentiment classification. This work evaluates ten machine learning algorithms, combining text preprocessing with text-network construction via word co-occurrence. Network topology and node-attribute measures are extracted and used to predict sentiment on Yelp reviews. Integrating network-derived features improves results, reaching an AUC of 83%, highlighting their value for sentiment tasks.","Improving sentiment analysis using text network features within different machine learning algorithms  \nAli Mohamed Alnasrawi1, Asia Mahdi Naser Alzubaidi2, Ahmed Abdulhadi Al-Moadhen3 1Department of Information Technology, Faculty of Computer Science and Information Technology, University ofKerbala,  \nKerbala, Iraq  \n2Department of Computer Science, Faculty of Computer Science and Information Technology, University ofKerbala, Kerbala, Iraq 3Department of Electrical and Electronics Engineering, Faculty of Engineering, University ofKerbala, Kerbala, Iraq  \nArticle history:  \nReceived Dec 21, 2022 Revised May 24, 2023 Accepted Jun 5, 2023  \nKeywords:  \nNetwork analysis Sentiment analysis  \nText network  \nTraditional machine learning Yelp dataset  \nCorresponding Author:  \nSentiment analysis poses a significant challenge due to the inherent subjectivity of natural language and the prevalence of unstandardized dialects in social networks. Regrettably, existing literature lacks a dedicated focus on network representation learning for sentiment classification. This paper addresses this gap by investigating ten machine learning algorithms, including support vector machine (SVM), random forest (RF), logistic regression (LR), and Naive Bayes (NB) . Our approach integrates text network analysis and sentiment analysis to propose a comprehensive solution. We begin by applying text preprocessing techniques and converting a text corpus into a text network using word co-occurrence. Subsequently, we employ network analysis techniques to extract features based on network topology and node attributes. These network-derived features serve as inputs for sentiment prediction on Yelp reviews. Through the incorporation of diverse text network features and various machine learning algorithms, we achieve significant enhancements in sentiment classification performance. Our evaluation demonstrates an improved area under curve (AUC) of 83% on the Yelp reviews corpus, underscoring the efficacy of integrating network features to enhance sentiment classifiers. This research underscores the critical role of network representation and its potential impact on sentiment analysis, highlighting the prospect of harnessing network features for sentiment classification tasks.  \nThis is an open access article under the CC BY-SA license.  \nAsia Mahdi Naser Alzubaidi  \nDepartment of Computer Science, Faculty of Computer Science and Information Technology University ofKerbala  \nKerbala, Iraq  \nEmail: [asia.m@uokerbala.edu.iq](asia.m@uokerbala.edu.iq)  \nArticle Info ABSTRACT  \n1. INTRODUCTION  \nSentiment analysis (SA), also called opinion mining is one of the most fundamental tasks in natural language processing (NLP) that deals with unstructured text and classifies it as expressing either a positive, negative, or neutral sentiment [1], [2] . SA has become an important tool for decision-makers and business executives, as well as for the general public, to grasp sentiments and attitudes. Because users are increasingly contacting one another before making purchasing decisions, decision-makers and corporate leaders are now investing heavily in assessing public opinion about their products and services [3] . They invest in SA not only to keep their consumers happy but also to develop new products, services and attract new customers. In  \npolitics, it can be used to infer popular attitudes and reactions to political events, allowing better judgments tobe made. This fact pushes the NLP community to devote more resources to SA research [4] .  \nResearchers have recently presented many ways for automatically classifying opinionated texts as positive, negative or neutral. Essentially, there are two main approaches, the first is utilizing machine learning (ML) algorithms, which are presented in this paper, and the second is utilizing lexicon based (LB) approach works with the understanding that contextual sentiment orientation is the sum of the opinion orientation of every availabl","cbCaitnKiI9z6NGn","https://ap.wps.com/l/cbCaitnKiI9z6NGn","pdf",569432,1,"English","en",105,"# Introduction\n## Approaches to sentiment analysis\n## Challenges and motivation\n# Methodology and framework\n## Text preprocessing and text-network construction\n## Network feature extraction\n# Experimental evaluation\n## Algorithms comparison and results\n## Performance metrics (AUC)\n# Conclusion","[{\"question\":\"Why is sentiment analysis challenging in social networks?\",\"answer\":\"Sentiment analysis is affected by the subjectivity of natural language and the presence of unstandardized dialects across social platforms.\"},{\"question\":\"How does the proposed method represent text networks?\",\"answer\":\"It preprocesses the corpus, converts text into a network using word co-occurrence, then derives features from network topology and node attributes.\"},{\"question\":\"What dataset and performance result are used to validate the approach?\",\"answer\":\"The method predicts sentiment on Yelp reviews, achieving an improved area under the curve (AUC) of 83%.\"}]","Improving sentiment analysis using text network features within different machine learning algorithms | 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is sentiment analysis challenging in social networks?","Question",{"text":74,"@type":75},"Sentiment analysis is affected by the subjectivity of natural language and the presence of unstandardized dialects across social platforms.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"How does the proposed method represent text networks?",{"text":79,"@type":75},"It preprocesses the corpus, converts text into a network using word co-occurrence, then derives features from network topology and node attributes.",{"name":81,"@type":72,"acceptedAnswer":82},"What dataset and performance result are used to validate the approach?",{"text":83,"@type":75},"The method predicts sentiment on Yelp reviews, achieving an improved area under the curve (AUC) of 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