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With the exacerbation of global climate change and the growing environmental awareness among the general public, the concept of green consumption has gained significant attention across various sectors of society. As a representative example of green consumer products, energy-saving products play a crucial role in the timely realization of dual carbon goals. However, an analysis of online comments regarding energy-saving products reveals that the majority of these products still exhibit shortcomings in terms of efficacy, noise level, cost-effectiveness, and particularly, energy-saving appliances. This study focuses on the user-generated online comments data from the Taobao e-commerce platform for Grade 1 energy-saving refrigerators. By employing text mining techniques, the study aims to extract the essential information and sentiments expressed in the comments, in order to explore the consumption characteristics of Grade 1 energy-saving refrigerators. Moreover, the LBBA (LDA-Bert-BiLSTM-Attention) model is utilized to investigate the consumer topics of interest and emotional features. Initially, the LDA model is adopted to identify the attributes and weights of consumer concerns. Subsequently, the Bert model is pre-trained with the online comment data, and combined with the BiLSTM algorithm and Attention mechanism to predict sentiment categories. Finally, a transfer learning approach is utilized to determine the sentiment inclination of user-generated online comments and to identify the primary driving factors behind each sentiment category. This research employs sentiment analysis on online comments data regarding energy-saving products to uncover consumer sentiment attributes and emotional characteristics. It provides decision-makers with a comprehensive and systematic understanding of public consumption intentions, offering decision support for the efficient operation and management of the energy-saving product market., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

The label from industrial commodity packaging usually contains important data, such as production date, manufacturer, and other commodity-related information. As such, those labels are essential for consumers to purchase goods, help commodity supervision, and reveal potential product safety problems. Consequently, packaging label detection, as the prerequisite for product label identification, becomes a very useful application, which has achieved promising results in the past decades. Yet, in complex industrial scenarios, traditional detection methods are often unable to meet the requirements, which suffer from many problems of low accuracy and efficiency. In this paper, we propose a multifeature fast and attention-based algorithm using a combination of area suggestion and semantic segmentation. This algorithm is an attention-based efficient and multifeature fast text detector (termed AEMF). The proposed approach is formed by fusing segmentation branches and detection branches with each other. Based on the original algorithm that can only detect text in any direction, it is possible to detect different shapes with a better accuracy. Meanwhile, the algorithm also works better on long-text detection. The algorithm was evaluated using ICDAR2015, CTW1500, and MSRA-TD500 public datasets. The experimental results show that the proposed multifeature fusion with self-attention module makes the algorithm more accurate and efficient than existing algorithms. On the MSRA-TD500 dataset, the AEMF algorithm has an F-measure of 72.3% and a frame per second (FPS) of 8. On the CTW1500 dataset, the AEMF algorithm has an F-measure of 62.3% and an FPS of 23. In particular, the AEMF algorithm has achieved an F-measure of 79.3% and an FPS of 16 on the ICDAR2015 dataset, demonstrating the excellent performance in detecting label text on industrial packaging. [ABSTRACT FROM AUTHOR]