Cognitive Computation最新文献

This study investigates the relationship between emotional trends derived from X platform data and the market dynamics of prominent cryptocurrencies—Cardano, Binance, Fantom, Matic, and Ripple—during the period from October 2022 to March 2023. Utilizing SenticNet, key emotions such as fear and anxiety, rage and anger, grief and sadness, delight and pleasantness, enthusiasm and eagerness, and delight and joy were identified. The emotional data and cryptocurrency price data, sourced bi-weekly, were analyzed to uncover significant correlations. The findings reveal that emotions such as delight and pleasantness and delight and joy have the strongest positive correlations with Fantom’s price, while delight and pleasantness exhibit the strongest negative correlations with Cardano and Binance. The study highlights the nuanced impact of specific emotional states on cryptocurrency prices, offering valuable insights for market participants.

This paper addresses the notable gap in evaluating eXplainable Artificial Intelligence (XAI) methods for text classification. While existing frameworks focus on assessing XAI in areas such as recommender systems and visual analytics, a comprehensive evaluation is missing. Our study surveys and categorises recent post hoc XAI methods according to their scope of explanation and output format. We then conduct a systematic evaluation, assessing the effectiveness of these methods across varying scopes and levels of output granularity using a combination of objective metrics and user studies. Key findings reveal that feature-based explanations exhibit higher fidelity than rule-based ones. While global explanations are perceived as more satisfying and trustworthy, they are less practical than local explanations. These insights enhance understanding of XAI in text classification and offer valuable guidance for developing effective XAI systems, enabling users to evaluate each explainer’s pros and cons and select the most suitable one for their needs.

As cognitive-inspired computation approaches, deep neural networks or deep learning (DL) models have played important roles in allowing machines to reach human-like performances in various complex cognitive tasks such as cognitive computation and sentiment analysis. This paper offers a thorough examination of the rapidly developing topic of DL-assisted aspect-based sentiment analysis (DL-ABSA), focusing on its increasing importance and implications for practice and research advancement. Leveraging bibliometric indicators, social network analysis, and topic modeling techniques, the study investigates four research questions: publication and citation trends, scientific collaborations, major themes and topics, and prospective research directions. The analysis reveals significant growth in DL-ABSA research output and impact, with notable contributions from diverse publication sources, institutions, and countries/regions. Collaborative networks between countries/regions, particularly between the USA and China, underscore global engagement in DL-ABSA research. Major themes such as syntax and structure analysis, neural networks for sequence modeling, and specific aspects and modalities in sentiment analysis emerge from the analysis, guiding future research endeavors. The study identifies prospective avenues for practitioners, emphasizing the strategic importance of syntax analysis, neural network methodologies, and domain-specific applications. Overall, this study contributes to the understanding of DL-ABSA research dynamics, providing a roadmap for practitioners and researchers to navigate the evolving landscape and drive innovations in DL-ABSA methodologies and applications.

Social cognition proposed that individual cognitive psychology was closely related to decision-making behavior. The heterogeneity of individual cognitive psychology has been ignored in large-scale decision-making. This research proposes a novel consensus decision model based on cognitive psychological state computation. Effective trust, cognitive trust, and opinion similarity are integrated to construct a fusion relationship network, and Louvain algorithm is used to divide communities. On this basis, non-cooperative individuals are identified. We quantify and classify individual cognitive psychological states by introducing attitude-belief factors. In this process, the cognitive trust and cognitive expression involved have fuzziness and uncertainty, which are quantified and computed by intuitionistic fuzzy set theory. Considering the difference in cognitive dissonance among non-cooperative individuals with different cognitive states, an adaptive feedback mechanism and trust renewal rule are proposed. The simulation results show that, on the one hand, the consensus model in this paper has a high timeliness. On the other hand, among the four types of cognitive psychological state, the non-cooperative individual with higher attitude factor and lower belief factor had higher management efficiency and consensus-reaching speed.

Document-level relation extraction (RE) is a complex and significant natural language processing task, as the massive entity pairs exist in the document and are across sentences in reality. However, the existing relation extraction methods (deep learning) often use single-view information (e.g., entity-level or sentence-level) to learn the relational information but ignore the multi-view information, and the explanations of deep learning are difficult to be reflected, although it achieves good results. To extract high-quality relational information from the document and improve the explanations of the model, we propose a multi-view cooperative learning with invariant rationale (MCLIR) framework. Firstly, we design the multi-view cooperative learning to find latent relational information from the various views. Secondly, we utilize invariant rationale to encourage the model to focus on crucial information, which can empower the performance and explanations of the model. We conduct the experiment on two public datasets, and the results of the experiment demonstrate the effectiveness of MCLIR.