Hei-Chia Wang

Writing the Related Work section is a challenging task for inexperienced researchers, requiring significant time, effort, and skill. This study aims to ease this burden by introducing an ICT-based framework that automates the generation of cohesive and well-structured Related Work sections. We propose SciRelate, a novel framework that combines abstractive multidocument summarization and automated keyword-based subsection heading extraction. SciRelate comprises three main modules: (1) document clustering using Latent Dirichlet Allocation (LDA), (2) related Work generation via a fine-tuned Bidirectional and Autoregressive Transformer (BART) model, and (3) Topic keyword extraction using TopicFusion-MP, a hybrid method that integrates LDA, TextRank, and MPNET embeddings. Experiments were conducted using the NudtRwG dataset, and BART was evaluated using 10-fold cross-validation. LDA clustering effectively identified subsection headings aligned with ground truth structures. The BART model achieved high ROUGE scores: 81.53 (ROUGE-1), 76.19 (ROUGE-2), and 79.03 (ROUGE-L). TopicFusion-MP outperformed six baseline keyword extraction methods in four scenarios, reaching a top similarity score of 0.6398. This study presents a comprehensive, automated pipeline for generating Related Work sections, integrating clustering, summarization, and keyword extraction in a unified framework. SciRelate highlights the potential of ICT tools to support scientific writing, particularly for new researchers.

Sentiment analysis is a powerful tool for monitoring attitudes towards companies, products or services and identifying specific features that drive positive or negative sentiment. However, collecting labelled data for training sentiment analysis models in a specific domain can be challenging in practical applications. One promising solution to this ‘cold-start’ problem is domain adaptation, which leverages labelled data from a related source domain to train a model for the target domain. A critical yet often neglected aspect in prior research is the measurement of similarity between the source and target domains, a factor that greatly impacts the success of domain adaptation. To fill this gap, we propose a novel measure that combines semantic, syntactic and lexical features to assess corpus-level similarity between two domains. Our experimental results demonstrate that our method achieves high precision (0.91) and recall (0.75), outperforming traditional methods. Moreover, our proposed measure can assist new domain products in selecting the most suitable training data set for their sentiment analysis tasks.

The era of low-interest rates is coming. In addition to their basic salary, people hope to increase their income by doing part-time work, understanding how to use assets already on hand, and investing in assets to earn extra rewards. Goldman Sachs reports that over the past 140 years, the 10-year stock market return has averaged 9.2%. The investment firm also noted that the S&P 500 outperformed its 10-year historical average with an annual average return of 13.6% between 2010 and 2020. Nowadays, with increased computing power and advancements in artificial intelligence algorithms, the effective use of computing power for investment has become an important topic. In the investment process, venture capitalists form portfolios, a practice that improves investment efficiency and reduces risks in a relatively safe situation. Current investments are not limited to one country, allowing for investments in other countries. Given this situation, we must pay attention to Uncovered Equity Parity (UEP) conditions. Thus, we aim to find optimal dynamic trading strategies with Deep Reinforcement Learning, considering the aforementioned properties.

Purpose: The explosion of data due to the sophistication of information and communication technology makes it simple for prospective tourists to learn about previous hotel guests' experiences. They prioritize the rating score when selecting a hotel. However, rating scores are less reliable for suggesting a personalized preference for each aspect, especially when they are in a limited number. This study aims to recommend ratings and personalized preference hotels using cross-domain and aspect-based features.Design/methodology/approach: We propose an aspect-based cross-domain personalized recommendation (AsCDPR), a novel framework for rating prediction and personalized customer preference recommendations. We incorporate a cross-domain personalized approach and aspect-based features of items from the review text. We extracted aspect-based feature vectors from two domains using bidirectional long short-term memory and then mapped them by a multilayer perceptron (MLP). The cross-domain recommendation module trains MLP to analyze sentiment and predict item ratings and the polarities of the aspect based on user preferences.Findings: Expanded by its synonyms, aspect-based features significantly improve the performance of sentiment analysis on accuracy and the F1-score matrix. With relatively low mean absolute error and root mean square error values, AsCDPR outperforms matrix factorization, collaborative matrix factorization, EMCDPR and Personalized transfer of user preferences for cross-domain recommendation. These values are 1.3657 and 1.6682, respectively.Research limitation/implications: This study assists users in recommending hotels based on their priority preferences. Users do not need to read other people's reviews to capture the key aspects of items. This model could enhance system reliability in the hospitality industry by providing personalized recommendations.Originality/value: This study introduces a new approach that embeds aspect-based features of items in a cross-domain personalized recommendation. AsCDPR predicts ratings and provides recommendations based on priority aspects of each user's preferences.