Analysing the sentiment of air-traveller: a comparative analysis

Airport service quality is considered to be an indicator of passenger satisfaction. However, assessing this by conventional methods requires continuous observation and monitoring. Therefore, during the past few years, the use of machine learning techniques for this purpose has attracted considerable attention for analysing the sentiment of the air traveller. A sentiment analysis system for textual data analytics leverages the natural language processing and machine learning techniques in order to determine whether a piece of writing is positive, negative or neutral. Numerous methods exist for estimating sentiments which include lexical-based methodologies and directed artificial intelligence strategies. Despite the wide use and ubiquity of certain strategies, it remains unclear which is the best strategy for recognising the intensity of the sentiments of a message. It is necessary to compare these techniques in order to understand their advantages, disadvantages and limitations. In this paper, we compared the Valence Aware Dictionary and sentiment Reasoner, a sentiment analysis technique specifically attuned and well known for performing good on social media data, with the conventional machine learning techniques of handling the textual data by converting it into numerical form. We used the review data obtained from the SKYTRAX website for each airport. The machine learning algorithms evaluated in this paper are VADER sentiment and logistic regression. The term frequency-inverse document frequency is used in order to convert the textual review data into the resulting numerical columns. This was formulated as a classification problem, whereby the prediction of the algorithm was compared with the actual recommendation of the passenger in the dataset. The results were analysed according to the accuracy, precision, recall and F1-score. From the analysis of the results, we observed that logistic regression outperformed the VADER sentiment analysis.Homaid M, Bisandu D, Moulitsas I, Jenkins K. (2022) Analysing the sentiment of air-traveller: a comparative analysis, International Journal of Computer Theory and Engineering, Volume 14, Issue 2, May 2022, pp. 48-5

Design exploration and performance strategies towards power-efficient FPGA-based achitectures for sound source localization

Many applications rely on MEMS microphone arrays for locating sound sources prior to their execution. Those applications not only are executed under real-time constraints but also are often embedded on low-power devices. These environments become challenging when increasing the number of microphones or requiring dynamic responses. Field-Programmable Gate Arrays (FPGAs) are usually chosen due to their flexibility and computational power. This work intends to guide the design of reconfigurable acoustic beamforming architectures, which are not only able to accurately determine the sound Direction-Of-Arrival (DoA) but also capable to satisfy the most demanding applications in terms of power efficiency. Design considerations of the required operations performing the sound location are discussed and analysed in order to facilitate the elaboration of reconfigurable acoustic beamforming architectures. Performance strategies are proposed and evaluated based on the characteristics of the presented architecture. This power-efficient architecture is compared to a different architecture prioritizing performance in order to reveal the unavoidable design trade-offs

Effects of innovation types on firm performance

Innovation is broadly seen as an essential component of competitiveness, embedded in the organizational structures, processes, products, and services within a firm. The objective of this paper is to explore the effects of the organizational, process, product, and marketing innovations on the different aspects of firm performance, including innovative, production, market, and financial performances, based on an empirical study covering 184 manufacturing firms in Turkey. A theoretical framework is empirically tested identifying the relationships amid innovations and firm performance through an integrated innovation-performance analysis. The results reveal the positive effects of innovations on firm performance in manufacturing industries

Assessment for learning and for self-regulation

Drawing on a research study of formative assessment practices in Irish schools, this paper traces the design, development and pilot of the Assessment for Learning Audit instrument (AfLAi) - a research tool for measuring teachers’ understanding and deployment of formative teaching, learning and assessment practices. Underpinning the paper is an extensive body of international research connecting assessment for learning pedagogy with student self-regulation, mental health and well-being. Reflecting on the potential of the AfLAi as a research tool, an activity systems framework is advanced as a mechanism to engage researchers and teachers in meaningful site-based continuous professional development that supports teachers’ interrogation of aggregated school data derived from their responses to the AfLAi. It is argued that by de-privatising classroom practice in this way and challenging teachers to examine self-reports of their understanding and use of assessment for learning pedagogy, the extent to which students are afforded opportunities to develop as self-regulating learners is laid bare. In turn, the teaching, learning and assessment conditions that serve to create and sustain selfregulation by students emerge. The paper is premised on a commitment to a biopsychosocial approach to mental health and to an inter-disciplinary, multi-lens, research agenda that will yield comprehensive, dynamic insights and understandings to inform future practice.peer-reviewe