In the contemporary landscape of financial systems and digital innovations, the convergence of blockchain technology and crowdfunding models has unveiled a promising avenue for revolutionizing traditional fundraising ...
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The research addresses the need for automated sentiment analysis (SA) due to the impracticality of manual analysis given the vast amount of online customer feedback. Traditional sentiment analysis methods, which focus...
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ISBN:
(纸本)9798331530402
The research addresses the need for automated sentiment analysis (SA) due to the impracticality of manual analysis given the vast amount of online customer feedback. Traditional sentiment analysis methods, which focus on overall sentiment at the sentence or document level, fail to capture detailed insights within customer reviews. To overcome this, the research develops an aspect-level sentiment analysis (ALSA) system using supervised learning techniques. This system aims to identify specific aspects of products or services and evaluate the sentiment expressed towards each aspect, offering a more detailed understanding of customer opinions. The core of the research is a Multivariate Feature Selection Framework for ALSA, which enhances sentiment classification by combining filter and wrapper methods to identify the most relevant features for each aspect. Key components of the framework include Aspect Extraction Using POS tagging and dependency parsing to identify aspects in the text, Feature Extraction for Retaining features that are highly discriminative for aspect-specific sentiment analysis, and Recursive Feature Elimination (RFE): Refine the set of features based on its impact on the performance of the machine learning model. The framework is evaluated on benchmark ALSA datasets, showing significant improvements in sentiment classification accuracy over baseline models. It also provides insights into the importance of selected features for different aspects and sentiment polarities, improves accuracy and interpretability, and reduces computational costs by removing redundant features. The ALSA system involves several subtasks, such as extraction of aspects from reviews, extraction of aspects terms, classification of categories, classification of perceived polarity at the aspect level, and calculation of perception score. The approach first extracts aspects and detects sentiment polarity, then extracts relevant features, uses a model for sentiment classification,
Water is a significant resource in day-to-day life, and it usually requires technological association for comprehensive management. Smart Water Grids (SWG) typically use cyberphysical systems (CPS) to monitor several ...
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In the present day and age of technology and digitalization our proposed platform revolutionizes the agricultural market concept by providing a direct connection between the farmers and the consumers thus reducing the...
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This paper describes a blockchain-based patient record management system that combines a React online user interface with a MongoDB database, IPFS, Hardhat, MetaMask, IPFS smart contracts, and the Ethereum blockchain....
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This paper introduces an innovative Voice-Controlled Google Sheets Extension employing Natural Language Processing (NLP) capabilities. The system seamlessly integrates a Chrome extension and a meticulously trained Nam...
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Deep learning has a variety of uses and issues it can solve in the real world, but it also has some limitations. The growing use of AI-Morped Videos is one of the most recent and complicated issues. "AI Morphed V...
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Airplanes play a critical role in global transportation, ensuring the efficient movement of people and goods. Although generally safe, aviation systems occasionally encounter incidents and accidents that underscore th...
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Due to the rising sale of cars, the price of secondhand cars has evolved into a significant concern, which may negatively impact sustainability. Using online platforms to determine the price of used vehicles has becom...
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The rapid development of digital media has enabled users to access a colossal choice of music through the internet and electronic devices. Music recommendations are given mostly based on the user’s past listening his...
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