International Journal of Web Engineering and Technology
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International Journal of Web Engineering and Technology (4 papers in press)
A Hybrid Approach for Aspect-Based Sentiment Analysis Using A Double Rotatory Attention Model by Guangyao Zhou, Jingyi Cheng, Flavius Frasincar Abstract: Nowadays, the web is an essential hub for gathering comments on entities and their associated aspects. In this paper, we propose a model which is capable of extracting these opinions and predicting the sentiment scores in aspect-level sentiment mining. In our two-step approach, a lexicalised domain ontology is firstly applied for sentiment classification. If the result is inconclusive from the first step, the backup model double rotatory attention mechanism is applied, which utilises deep contextual word embeddings to better capture the (multi-)word semantics in the given text. This study contributes to the current research by introducing novel repetition and rotatory structures to refine the attention mechanism. It is shown that our model outperforms state-of-the-art methods on the datasets of SemEval 2015 and SemEval 2016. Keywords: LCR-Rot; double rotatory attention; DRA; contextual word embeddings; BERT; sentiment analysis; classification; lexicalised domain ontology; hierarchical attention; hybrid approach.
The Role of Sentiment Analysis in a Recommender System: A systematic survey by Jitali Patel, Hitesh Chhinkaniwala Abstract: Currently, fields like e-commerce, education, social media, tourism, and the entertainment industry rely on recommender systems to provide personalised services to their clients. The most common and widely accepted technique - collaborative filtering, creates recommendations by examining the users past rating patterns. Collaborative filtering assumes that a users past rating data accurately reflects their actual preferences. However, different study found that the ratings may not accurately reflect user preferences in the real-world circumstances. Therefore, to deal with this problem, sentiment analysis of user-generated text is started to be used. It helps to improve the performance of recommender systems, as it provides more specific and trustworthy user preferences than ratings. The rich information in user-generated text like reviews/comments is analysed through sentiment analysis and fused into recommender systems. A recommender system implemented with sentiment analysis is called a sentiment-aware/enhanced recommender system. A sentiment-aware recommender system captures sentiment from the user-generated content and provides most suited personalised services to the user. It alleviates several problems like sparsity, cold start, rating bias, and one class collaborative filtering and enhances recommendation performance. This survey presents the application domain of a sentiment enhanced recommender system. We have also discussed various pre-processing techniques applied to user-generated text. We have classified sentiment enhanced recommender systems according to the level of sentiment analysis and presented technical aspects such as datasets, methodologies and results. Different strategies of how sentiment analysis can be incorporated into recommender systems have been analysed. We have presented research opportunities in this field based on a systematic survey, and we hope our survey is utilised to fill existing gaps and enhance its applicability. Keywords: Recommender system; Sentiment Analysis; User-generated text; Collaborative filtering; Sentiment-aware recommender system.
Website user experience model: testing on journalists by Purwadi Purwadi, Irwansyah Irwansyah Abstract: This research aimed to analyze the components of the website user experience (WUX) and the influence of WUX on brand trust. Another objective was to build a WUX model from the perspective of journalist users. This research approach was quantitative with an online survey method. The research sample was 300 journalists. Partial Least Square-Structural Equation Modeling (PLS-SEM) was used for data processing techniques and hypothesis testing. This research found two alternative WUX models. The first alternative WUX model showed a significant relationship between WUX (with six components in WUX) to brand trust. The second alternative WUX model showed a meaningful relationship between access speed, user value, user's emotion (three components in the WUX framework that were treated as variables) to brand trust. Of the two models, this research recommended the first alternative WUX model because the components of WUX were better and more complex. Keywords: user experience; website; communication; website user experience; brand trust.
Using Seagull Optimization Algorithm to Select Mobile Service in Cloud and Edge Computing Environment by Feilong Yu, Jing Li, ming zhu, Xiukun Yan Abstract: With the rapid development of edge computing, more and more services are deployed on edge servers. Compared with traditional cloud computing, services in the edge computing environment are closer to users, which brings benefits of high performance and low latency to the user-service interactions. However, due to the limited resources of edges, services provided by edges alone may fail to meet increasingly complex mobile computing requirements, therefore, services on clouds become an effective supplement. With the massive increment of services in the mobile Internet, selecting proper services to fulfill mobile users’ requests becomes a key research field. This paper proposes a service selection model for mobile service selection problem in cloud and edge computing environment. The proposed model combines the seagull optimization algorithm and the simulated annealing algorithm. Through comparative experiments on
simulation datasets with referencing to some other service selection models, it
can be inferred that the proposed selection model finds a solution with better QoS
performance in fewer iterations. Keywords: Mobile Edge Computing; Cloud Computing; Seagull Optimization Algorithm; Service Selection