Forecasting the U.S. oil markets based on social media information during the COVID-19 pandemic

Climate change, the growth in world population, high levels of food waste and food loss, and the risk of new disease or pandemic outbreaks are examples of the many challenges that threaten future food sustainability and the security of the planet and urgently need to be addressed. The fourth industrial revolution, or Industry 4.0, has been gaining momentum since 2015, being a significant driver for sustainable development and a successful catalyst to tackle critical global challenges. This review paper summarizes the most relevant food Industry 4.0 technologies including, among others, digital technologies (e.g., artificial intelligence, big data analytics, Internet of Things, and blockchain) and other technological advances (e.g., smart sensors, robotics, digital twins, and cyber-physical systems). Moreover, insights into the new food trends (such as 3D printed foods) that have emerged as a result of the Industry 4.0 technological revolution will also be discussed in Part II of this work. The Industry 4.0 technologies have significantly modified the food industry and led to substantial consequences for the environment, economics, and human health. Despite the importance of each of the technologies mentioned above, ground-breaking sustainable solutions could only emerge by combining many technologies simultaneously. The Food Industry 4.0 era has been characterized by new challenges, opportunities, and trends that have reshaped current strategies and prospects for food production and consumption patterns, paving the way for the move toward Industry 5.0.

The fourth industrial revolution in the food industry-Part I: Industry 4.0 technologies.

Social media mining for ideation: Identification of sustainable solutions and opinions

Sourcing product innovation intelligence from online reviews

With the rapid development of the Internet, the massive amount of web textual data has grown exponentially, which has brought considerable challenges to downstream tasks, such as document management, text classification, and information retrieval. Automatic text summarization (ATS) is becoming an extremely important means to solve this problem. The  core of ATS is to mine the gist of the original text and automatically generate a concise and readable summary. Recently, to better balance and develop these two aspects, deep learning (DL)-based abstractive summarization models have been developed. At present, for ATS tasks, almost all state-of-the-art (SOTA) models are based on DL architecture. However, a comprehensive literature survey is still lacking in the field of DL-based abstractive text summarization. To fill this gap, this paper provides researchers with a comprehensive survey of DL-based abstractive summarization. We first give an overview of abstractive summarization and DL. Then, we summarize several typical frameworks of abstractive summarization. After that, we also give a comparison of several popular datasets that are commonly used for training, validation, and testing. We further analyze the performance of several typical abstractive summarization systems on common datasets. Finally, we highlight some open challenges in the abstractive summarization task and outline some future research trends. We hope that these explorations will provide researchers with new insights into DL-based abstractive summarization.

/pdf/a-comprehensive-survey-of-abstractive-text-summarization-1gl0p5v0.pdf

A Comprehensive Survey of Abstractive Text Summarization Based on Deep Learning

The importance of online customer reviews to product innovation has been well-recognized in prior literature. Mining online reviews has received extensive attention and efforts. Most existing research on mining online reviews focus on issues such as the impact of reviews on sales, helpfulness of reviews, and customers’ participation in reviews. Few research studies, however, seek to identify and extract innovation ideas for products from online reviews. This type of information is particularly important for product functionality improvement and new feature development from a manufacturer's perspective. Mining product innovation ideas allows a manufacturer to proactively review customer opinion and unlock insights about new functionality and features that the market expects, in order to gain a competitive advantage. In this paper, we propose a deep learning-based approach to identify sentences that contain innovation ideas from online reviews. Specifically, we develop a novel ensemble embedding method to generate semantic and contextual representations of the words in review sentences. The resultant representations in each sentence are then used in a long short-term memory (LSTM) model for innovation-sentence identification. Moreover, we adopt a focal loss function in our model to address the class imbalance problem. We validate our approach with a dataset of 10,000 customer reviews from Amazon. Our model achieves an AUC score of 0.91 and an F1 score of 0.89, outperforming a set of state-of-the-art baseline models in the comparison. Our approach can be extended and applied to many other information extraction tasks.

Wenjun Wang

Papers

Mining product innovation ideas from online reviews