Pub Date : 2026-01-05DOI: 10.1007/s40747-025-02210-2
Zhiqiang Gao
{"title":"A chord-controlled transformer for controllable and coherent music generation","authors":"Zhiqiang Gao","doi":"10.1007/s40747-025-02210-2","DOIUrl":"https://doi.org/10.1007/s40747-025-02210-2","url":null,"abstract":"","PeriodicalId":10524,"journal":{"name":"Complex & Intelligent Systems","volume":"42 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2026-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145902510","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-05DOI: 10.1007/s40747-025-02222-y
Kechao Li, Nor Ashikin Mohamad Kamal
{"title":"Deep recommendation algorithm based on reviews and descriptions neural matrix factorization under cold-start","authors":"Kechao Li, Nor Ashikin Mohamad Kamal","doi":"10.1007/s40747-025-02222-y","DOIUrl":"https://doi.org/10.1007/s40747-025-02222-y","url":null,"abstract":"","PeriodicalId":10524,"journal":{"name":"Complex & Intelligent Systems","volume":"83 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2026-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145902512","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-29DOI: 10.1007/s40747-025-02173-4
Yinglong Yu, Hao Shen, Ming Yang, Yu Wang, Yanyu Liu
{"title":"ProTriPlay: A trinity framework for professional interactive theater based on LLM","authors":"Yinglong Yu, Hao Shen, Ming Yang, Yu Wang, Yanyu Liu","doi":"10.1007/s40747-025-02173-4","DOIUrl":"https://doi.org/10.1007/s40747-025-02173-4","url":null,"abstract":"","PeriodicalId":10524,"journal":{"name":"Complex & Intelligent Systems","volume":"88 7 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2025-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145847119","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper presents LISA as a Language Independent Sentiment Analysis tool that exploits Graph Neural Networks, GNN, for sentiment analysis, SA, applications. To build up that analyzer two types of GNN are examined. These types are: (1) Graph convolutional Networks, GCN, and (2) Graph Attention Networks, GAT. The input text is transformed to a corresponding graph to bypass the multilingual differential representations between Natural Languages, NL. Accordingly, LISA includes input steam of text (e.g. tweets, reviews…), followed by a text to graph module which is connected to a tokenizer to determine the correct tokens and pass them to Word2vec embedding. The corresponding graph of the underlying text enters the GNN which has been implemented by either GCN or GAT to generate the output sentiment polarity (+ ve/−ve). The two GNN models are compared to explore the effect of attention on a SA. In addition, LISA performance is evaluated (using two multilingual datasets: amazon reviews, tweets). It has been found that its accuracy, F1 score and confusion matrices are in the same range of the state of the art (SOTA) framework.
{"title":"LISA: language independent sentiment analysis using graph neural networks","authors":"Mohamed Zaki, Basheer Youssef, Salwa El-gamal, Mohamed Abd-Elrahem","doi":"10.1007/s40747-025-02145-8","DOIUrl":"https://doi.org/10.1007/s40747-025-02145-8","url":null,"abstract":"This paper presents LISA as a Language Independent Sentiment Analysis tool that exploits Graph Neural Networks, GNN, for sentiment analysis, SA, applications. To build up that analyzer two types of GNN are examined. These types are: (1) Graph convolutional Networks, GCN, and (2) Graph Attention Networks, GAT. The input text is transformed to a corresponding graph to bypass the multilingual differential representations between Natural Languages, NL. Accordingly, LISA includes input steam of text (e.g. tweets, reviews…), followed by a text to graph module which is connected to a tokenizer to determine the correct tokens and pass them to Word2vec embedding. The corresponding graph of the underlying text enters the GNN which has been implemented by either GCN or GAT to generate the output sentiment polarity (+ ve/−ve). The two GNN models are compared to explore the effect of attention on a SA. In addition, LISA performance is evaluated (using two multilingual datasets: amazon reviews, tweets). It has been found that its accuracy, F1 score and confusion matrices are in the same range of the state of the art (SOTA) framework.","PeriodicalId":10524,"journal":{"name":"Complex & Intelligent Systems","volume":"14 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2025-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145847120","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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