A HYBRID DEEP LEARNING APPROACH FOR SENTIMENT ANALYSIS IN PRODUCT REVIEWS

Product reviews play a crucial role in providing valuable insights to consumers and producers. Analyzing the vast amount of data generated around a product, such as posts, comments, and views, can be challenging for business intelligence purposes. Sentiment analysis of this content helps both consumers and producers gain a better understanding of the market status, enabling them to make informed decisions. In this study, we propose a novel hybrid approach based on deep neural networks (DNNs) for sentiment analysis in product reviews, focusing on the classification of sentiments expressed. Our approach utilizes the recursive neural network (RNN) algorithm for sentiment classification. To address the imbalanced distribution of positive and negative samples in social network data, we employ a resampling technique that balances the dataset by increasing samples from the minority class and decreasing samples from the majority class. We evaluate our approach using Amazon data, comprising four product categories: clothing, cars, luxury goods, and household appliances. Experimental results demonstrate that our proposed approach performs well in sentiment analysis for product reviews, particularly in the context of digital marketing. Furthermore, the attention-based RNN algorithm outperforms the baseline RNN by approximately 5%. Notably, the study reveals consumer sentiment variations across different products, particularly in relation to appearance and price aspects

Axiological entanglement of economics

In mainstream economics, the principle of freedom from value judgment (Wertfreiheit) is enforced. This principle has different interpretations. The aim of this paper is to present the author’s interpretations of the principle of Wertfreiheit. The basic ideas of axiology have been analysed: values, valuations, and value judgments, including their application to economic activities and economic researches. Two models of valuations have been presented—the taste model and perception model—as well as points of view of economic researches on those models. The conclusion comprises the author’s interpretation of the principle of Wertfreiheit as that of impartiality and integrity in research activities.Publication of English-language versions of the volumes of the “Annales. Ethics in Economic Life” financed through contract no. 501/1/P-DUN/2017 from the funds of the Ministry of Science and Higher Education devoted to the promotion of scholarship

Deep learning in hospitality and tourism:a research framework agenda for future research

PurposeThis study aims to provide a systematic review of the existing literature on the applications of deep learning (DL) in hospitality, tourism and travel as well as an agenda for future research.Design/methodology/approachCovering a five-year time span (2017–2021), this study systematically reviews journal articles archived in four academic databases: Emerald Insight, Springer, Wiley Online Library and ScienceDirect. All 159 articles reviewed were characterised using six attributes: publisher, year of publication, country studied, type of value created, application area and future suggestions (and/or limitations).FindingsFive application areas and six challenge areas are identified, which characterise the application of DL in hospitality, tourism and travel. In addition, it is observed that DL is mainly used to develop novel models that are creating business value by forecasting (or projecting) some parameter(s) and promoting better offerings to tourists.Research limitations/implicationsAlthough a few prior papers have provided a literature review of artificial intelligence in tourism and hospitality, none have drilled-down to the specific area of DL applications within the context of hospitality, tourism and travel.Originality/valueTo the best of the authors’ knowledge, this paper represents the first theoretical review of academic research on DL applications in hospitality, tourism and travel. An integrated framework is proposed to expose future research trajectories wherein scholars can contribute significant value. The exploration of the DL literature has significant implications for industry and practice, given that this, as far as the authors know, is the first systematic review of existing literature in this research area

Deep learning in hospitality and tourism:a research framework agenda for future research

PurposeThis study aims to provide a systematic review of the existing literature on the applications of deep learning (DL) in hospitality, tourism and travel as well as an agenda for future research.Design/methodology/approachCovering a five-year time span (2017–2021), this study systematically reviews journal articles archived in four academic databases: Emerald Insight, Springer, Wiley Online Library and ScienceDirect. All 159 articles reviewed were characterised using six attributes: publisher, year of publication, country studied, type of value created, application area and future suggestions (and/or limitations).FindingsFive application areas and six challenge areas are identified, which characterise the application of DL in hospitality, tourism and travel. In addition, it is observed that DL is mainly used to develop novel models that are creating business value by forecasting (or projecting) some parameter(s) and promoting better offerings to tourists.Research limitations/implicationsAlthough a few prior papers have provided a literature review of artificial intelligence in tourism and hospitality, none have drilled-down to the specific area of DL applications within the context of hospitality, tourism and travel.Originality/valueTo the best of the authors’ knowledge, this paper represents the first theoretical review of academic research on DL applications in hospitality, tourism and travel. An integrated framework is proposed to expose future research trajectories wherein scholars can contribute significant value. The exploration of the DL literature has significant implications for industry and practice, given that this, as far as the authors know, is the first systematic review of existing literature in this research area

The Rise of Liberal Utilitarianism: Bentham and Mill

My aim in this chapter is to push back against the tendency to emphasize Mill’s break from Bentham rather than his debt to him. Mill made important advances on Bentham’s views, but I believe there remains a shared core to their thinking—over and above their commitment to the principle of utility itself—that has been underappreciated. Essentially, I believe that the structure of Mill’s utilitarian thought owes a great debt to Bentham even if he filled in that structure with a richer conception of human nature and developed it in more liberal directions. This commonality is revealed, in particular, in Mill’s own institutional designs and practical reform proposals in Considerations on Representative Government and related writings. If this is right, then the tendency of interpreters to highlight their differences rather than their similarities has been to the detriment of both Mill and Bentham scholarship, and so to our understanding of the rise of liberal utilitarianism

Occasional white boarding : examining the effects of physics students\u27 understanding of motion graphs

The Modeling method of teaching has demonstrated well--‐documented success in the improvement of student learning. The teacher/researcher in this study was introduced to Modeling through the use of a technique called White Boarding. Without formal training, the researcher began using the White Boarding technique for a limited number of laboratory experiences with his high school physics classes. The question that arose and was investigated in this study is “What specific aspects of the White Boarding process support student understanding?” For the purposes of this study, the White Boarding process was broken down into three aspects – the Analysis of data through the use of Logger Pro software, the Preparation of White Boards, and the Presentations each group gave about their specific lab data. The lab used in this study, an Acceleration of Gravity Lab, was chosen because of the documented difficulties students experience in the graphing of motion. In the lab, students filmed a given motion, utilized Logger Pro software to analyze the motion, prepared a White Board that described the motion with position--‐time and velocity--‐time graphs, and then presented their findings to the rest of the class. The Presentation included a class discussion with minimal contribution from the teacher. The three different aspects of the White Boarding experience – Analysis, Preparation, and Presentation – were compared through the use of student learning logs, video analysis of the Presentations, and follow--‐up interviews with participants. The information and observations gathered were used to determine the level of understanding of each participant during each phase of the lab. The researcher then looked for improvement in the level of student understanding, the number of “aha” moments students had, and the students’ perceptions about which phase was most important to their learning. The results suggest that while all three phases of the White Boarding experience play a part in the learning process for students, the Presentations provided the most significant changes. The implications for instruction are discussed

DOMESTIC SUPPORT: ISSUES AND OPTIONS IN THE AGRICULTURAL NEGOTIATIONS

International Relations/Trade,