arXiv subjects

Huimin Chen

Publications and source records attributed to Huimin Chen.

22 records · Page 2Linked to original sources

arXiv2020

Country Image in COVID-19 Pandemic: A Case Study of China

Country image has a profound influence on international relations and economic development. In the worldwide outbreak of COVID-19, countries and their people display different reactions, resulting in diverse perceived images among foreign public. Therefore, in this study, we take China as a specific and typical case and investigate its image with aspect-based sentiment analysis on a large-scale Twitter dataset. To our knowledge, this is the first study to explore country image in such a fine-grained way. To perform the analysis, we first build a manually-labeled Twitter dataset with aspect-level sentiment annotations. Afterward, we conduct the aspect-based sentiment analysis with BERT to explore the image of China. We discover an overall sentiment change from non-negative to negative in the general public, and explain it with the increasing mentions of negative ideology-related aspects and decreasing mentions of non-negative fact-based aspects. Further investigations into different groups of Twitter users, including U.S. Congress members, English media, and social bots, reveal different patterns in their attitudes toward China. This study provides a deeper understanding of the changing image of China in COVID-19 pandemic. Our research also demonstrates how aspect-based sentiment analysis can be applied in social science researches to deliver valuable insights.

cs.CY↗

arXiv2019

Enhancing Stock Movement Prediction with Adversarial Training

This paper contributes a new machine learning solution for stock movement prediction, which aims to predict whether the price of a stock will be up or down in the near future. The key novelty is that we propose to employ adversarial training to improve the generalization of a neural network prediction model. The rationality of adversarial training here is that the input features to stock prediction are typically based on stock price, which is essentially a stochastic variable and continuously changed with time by nature. As such, normal training with static price-based features (e.g. the close price) can easily overfit the data, being insufficient to obtain reliable models. To address this problem, we propose to add perturbations to simulate the stochasticity of price variable, and train the model to work well under small yet intentional perturbations. Extensive experiments on two real-world stock data show that our method outperforms the state-of-the-art solution with 3.11% relative improvements on average w.r.t. accuracy, validating the usefulness of adversarial training for stock prediction task.

q-fin.TR↗

arXiv2017

Joint Smoothing, Tracking, and Forecasting Based on Continuous-Time Target Trajectory Fitting

We present a continuous time state estimation framework that unifies traditionally individual tasks of smoothing, tracking, and forecasting (STF), for a class of targets subject to smooth motion processes, e.g., the target moves with nearly constant acceleration or affected by insignificant noises. Fundamentally different from the conventional Markov transition formulation, the state process is modeled by a continuous trajectory function of time (FoT) and the STF problem is formulated as an online data fitting problem with the goal of finding the trajectory FoT that best fits the observations in a sliding time-window. Then, the state of the target, whether the past (namely, smoothing), the current (filtering) or the near-future (forecasting), can be inferred from the FoT. Our framework releases stringent statistical modeling of the target motion in real time, and is applicable to a broad range of real world targets of significance such as passenger aircraft and ships which move on scheduled, (segmented) smooth paths but little statistical knowledge is given about their real time movement and even about the sensors. In addition, the proposed STF framework inherits the advantages of data fitting for accommodating arbitrary sensor revisit time, target maneuvering and missed detection. The proposed method is compared with state of the art estimators in scenarios of either maneuvering or non-maneuvering target.

stat.AP↗

arXiv2015

Superconductivity in a new layered nickel-selenide CsNi2Se2

The physical properties of CsNi$_{2}$Se$_{2}$ were characterized by electrical resistivity, magnetization and specific heat measurements. We found that the stoichiometric CsNi$_{2}$Se$_{2}$ compound is a superconductor with a transition temperature \textit{T$_{c}$}=2.7K. A large Sommerfeld coefficient $γ$$_{n}$ ($\sim$77.90 mJ/mol$\cdot$K$^{-2}$), was obtained from the normal state electronic specific heat. However, the Kadowaki-Woods ratio of CsNi$_{2}$Se$_{2}$ was estimated to be about 0.041$\times$10$^{-5}$ $μΩ$$\cdot$cm(mol$\cdot$K/mJ)$^{2}$, indicating the absence of strong electron-electron correlations in this compound. In the superconducting state, we found that the zero-field electronic specific heat data, $C_{es}(T)$ (0.5K $\leq$ T $<$ 2.6K), can be well fitted with a two-gap BCS model. The comparison with the results of the density functional theory (DFT) calculations suggested that the large $γ$$_{n}$ in the nickel-selenide superconductors may be related to the large Density of States (DOS) at the fermi surface.

cond-mat.supr-con↗