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- 저자 : Faruque M. Abdullah (검색결과 3 건)
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Ascertaining polarity of public opinions on Bangladesh cricket using machine learning techniques
Faruque M. Abdullah,Rahman Saifur,Chakraborty Partha,Choudhury Tanupriya,Um Jung-Sup,Singh Thipendra Pal 대한공간정보학회 2022 Spatial Information Research Vol.30 No.2
In the present world, we are not only the consumers of information but creators as well. The virtual world of social media, which is considered a free open forum for discussion; provides its participants a chance to shape or re-shape the digital information by expressing opinions. These opinions generally contain different types of sentiments. Sentiment analysis is a tool that performs the computational study of identifying and extracting sentiment content of textual data that can be used to classify those public opinions posted on various topics in social media. In this paper, a sentiment polarity detection approach is presented, that detects the polarity of textual Facebook posts in Bangla containing people’s point of views on Bangladesh Cricket using three popular supervised machine learning algorithms named Naive Bayes (NB), support vector machines (SVM), and logistic regression (LR). Comparative result analysis is also provided between classifiers, where LR performed slightly better than SVM and NB by considering n-gram as a feature with an accuracy of 83% in the present world, we are not only the consumers of information but creators as well. The virtual world of social media, which is considered a free open forum for discussion; provides its participants a chance to shape or re-shape the digital information by expressing opinions. These opinions generally contain different types of sentiments. Sentiment analysis is a tool that performs the computational study of identifying and extracting sentiment content of textual data that can be used to classify those public opinions posted on various topics in social media. In this paper, a sentiment polarity detection approach is presented, that detects the polarity of textual Facebook posts in Bangla containing people’s point of views on Bangladesh Cricket using three popular supervised machine learning algorithms named Naive Bayes (NB), support vector machines (SVM), and logistic regression (LR). Comparative result analysis is also provided between classifiers, where LR performed slightly better than SVM and NB by considering n-gram as a feature with an accuracy of 83%.
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Ascertaining polarity of public opinions on Bangladesh cricket using machine learning techniques
Faruque M. Abdullah,Rahman Saifur,Chakraborty Partha,Choudhury Tanupriya,Um Jung-Sup,Singh Thipendra Pal 대한공간정보학회 2022 Spatial Information Research Vol.30 No.1
In the present world, we are not only the consumers of information but creators as well. The virtual world of social media, which is considered a free open forum for discussion; provides its participants a chance to shape or re-shape the digital information by expressing opinions. These opinions generally contain different types of sentiments. Sentiment analysis is a tool that performs the computational study of identifying and extracting sentiment content of textual data that can be used to classify those public opinions posted on various topics in social media. In this paper, a sentiment polarity detection approach is presented, that detects the polarity of textual Facebook posts in Bangla containing people’s point of views on Bangladesh Cricket using three popular supervised machine learning algorithms named Naive Bayes (NB), support vector machines (SVM), and logistic regression (LR). Comparative result analysis is also provided between classifiers, where LR performed slightly better than SVM and NB by considering n-gram as a feature with an accuracy of 83%.
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Sayam Abdullah,Rahman A. N. M. Masudur,Rahman Md. Sakibur,Smriti Shamima Akter,Ahmed Faisal,Rabbi Md. Fogla,Hossain Mohammad,Faruque Md. Omar 한국탄소학회 2022 Carbon Letters Vol.32 No.5
The utilization of carbonaceous reinforcement-based polymer matrix composites in structural applications has become a hot topic in composite research. Although conventional carbon fiber-reinforced polymer composites (CFRPs) have revolutionized the composite industry by offering unparalleled features, they are often plagued with a weak interface and lack of toughness. However, the promising aspects of carbon fiber-based fiber hybrid composites and hierarchical composites can compensate for these setbacks. This review provides a meticulous landscape and recent progress of polymer matrix-based different carbonaceous (carbon fiber, carbon nanotube, graphene, and nanodiamond) fillers reinforced composites’ mechanical properties. First, the mechanical performance of neat CFRP was exhaustively analyzed, attributing parameters were listed down, and CFRPs’ mechanical performance barriers were clearly outlined. Here, short carbon fiber-reinforced thermoplastic composite was distinguished as a prospective material. Second, the strategic advantages of fiber hybrid composites over conventional CFRP were elucidated. Third, the mechanical performance of hierarchical composites based on carbon nanotube (1D), graphene (2D) and nanodiamond (0D) was expounded and evaluated against neat CFRP. Fourth, the review comprehensively discussed different fabrication methods, categorized them according to performance and suggested potential future directions. From here, the review sorted out three-dimensional printing (3DP) as the most futuristic fabrication method and thoroughly delivered its pros and cons in the context of the aforementioned carbonaceous materials. To conclude, the structural applications, current challenges and future prospects pertinent to these carbonaceous fillers reinforced composite materials were elaborated
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