首页|Researcher at Babes-Bolyai University Has Published New Data on Machine Learning (Machine-Learning-Based Approaches for Multi- Level Sentiment Analysis of Romanian Reviews)
Researcher at Babes-Bolyai University Has Published New Data on Machine Learning (Machine-Learning-Based Approaches for Multi- Level Sentiment Analysis of Romanian Reviews)
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2024 FEB 20 (NewsRx) – By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Investigators publish new report on artificial intelligence. According to news reporting originating from Cluj Napoca, Romania, by NewsRx correspondents, research stated, “Sentiment analysis has increasingly gained significance in commercial settings, driven by the rising impact of reviews on purchase decision-making in recent years.” Financial supporters for this research include European Regional Development Fund. Our news editors obtained a quote from the research from Babes-Bolyai University: “This research conducts a thorough examination of the suitability of machine learning and deep learning approaches for sentiment analysis, using Romanian reviews as a case study, with the aim of gaining insights into their practical utility. A comprehensive, multi-level analysis is performed, covering the document, sentence, and aspect levels. The main contributions of the paper refer to the in-depth exploration of multiple sentiment analysis models at three different textual levels and the subsequent improvements brought with respect to these standard models. Additionally, a balanced dataset of Romanian reviews from twelve product categories is introduced. The results indicate that, at the document level, supervised deep learning techniques yield the best outcomes (specifically, a convolutional neural network model that obtains an AUC value of 0.93 for binary classification and a weighted average F1-score of 0.77 in a multi-class setting with 5 target classes), albeit with increased resource consumption. Favorable results are achieved at the sentence level, as well, despite the heightened complexity of sentiment identification.”
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