疫情防控背景下对《鼠疫》的解读

《鼠疫》是荒诞主义代表作家、"存在主义"文学大师加缪的代表作。该文从作品的人物、背景、情感三方面对比分析新冠肺炎疫情防控时期三种主要情感:人们的恐惧、流放感和同理心。     

HEMOS: A novel deep learning-based fine-grained humor detecting method for sentiment analysis of social media

In this paper we introduce HEMOS (Humor-EMOji-Slang-based) system for fine-grained sentiment classification for the Chinese language using deep learning approach. We investigate the importance of recognizing the influence of humor, pictograms and slang on the task of affective processing of the social media. In the first step, we collected 576 frequent Internet slang expressions as a slang lexicon; then, we converted 109 Weibo emojis into textual features creating a Chinese emoji lexicon. In the next step, by performing two polarity annotations with new “optimistic humorous type” and “pessimistic humorous type” added to standard “positive” and “negative” sentiment categories, we applied both lexicons to attention-based bi-directional long short-term memory recurrent neural network (AttBiLSTM) and tested its performance on undersized labeled data. Our experimental results show that the proposed method can significantly improve the state-of-the-art methods in predicting sentiment polarity on Weibo, the largest Chinese social network.     

基于微博核心实体的情感分析方法及引导机制研究

【目的/意义】微博作为国内主要的社交网络平台之一,其信息传播实时快速,去中心化,成为网络舆情传播 的重要媒介。面向微博进行舆情中心人物的识别以及公众情绪的挖掘对网络舆情的控制具有重要的实践意义。 【方法/过程】本文以新疆棉花事件为例,使用生命周期法对微博舆情演化过程进行划分,使用word2vec和k-means 模型提取事件生命周期中各阶段的舆情中心人物,采用一种结合词典与LSTM深度学习模型的情感分析方法,对各 舆情中心人物相关的评论情感进行极性分析。【结果/结论】所提出的方法能够挖掘面向特定事件的微博舆情中心 人物、公众的情感类型及情感强度,得到能够使舆情转好的引导方法。【创新/局限】本文创新性的将主题挖掘方法 运用于微博舆情中心人物的提取。在情感分析方法上,结合词典和深度学习方法,解决了深度学习方法进行情感 分析时需人工标注的局限性。此外,本文进行情感值计算时没有考虑到表情符号的作用,后续研究会进一步考虑 更加细粒度的情感分类。     

Does government information release really matter in regulating contagion-evolution of negative emotion during public emergencies? From the perspective of cognitive big data analytics

The breeding and spreading of negative emotion in public emergencies posed severe challenges to social governance. The traditional government information release strategies ignored the negative emotion evolution mechanism. Focusing on the information release policies from the perspectives of the government during public emergency events, by using cognitive big data analytics, our research applies deep learning method into news framing framework construction process, and tries to explore the influencing mechanism of government information release strategy on contagion-evolution of negative emotion. In particular, this paper first uses Word2Vec, cosine word vector similarity calculation and SO-PMI algorithms to build a public emergencies-oriented emotional lexicon; then, it proposes a emotion computing method based on dependency parsing, designs an emotion binary tree and dependency-based emotion calculation rules; and at last, through an experiment, it shows that the emotional lexicon proposed in this paper has a wider coverage and higher accuracy than the existing ones, and it also performs a emotion evolution analysis on an actual public event based on the emotional lexicon, using the emotion computing method proposed. And the empirical results show that the algorithm is feasible and effective. The experimental results showed that this model could effectively conduct fine-grained emotion computing, improve the accuracy and computational efficiency of sentiment classification. The final empirical analysis found that due to such defects as slow speed, non transparent content, poor penitence and weak department coordination, the existing government information release strategies had a significant negative impact on the contagion-evolution of anxiety and disgust emotion, could not regulate negative emotions effectively. These research results will provide theoretical implications and technical supports for the social governance. And it could also help to establish negative emotion management mode, and construct a new pattern of the public opinion guidance.     

基于情感语义空间的食品安全舆情情感分析

[目的/意义] 区别于单一维度的情感强度测度,基于情感语义空间的食品安全舆情情感分析从立体空间角度探析情感的细粒度表征及情感焦点,对政府及有关部门提升舆情治理水平具有重要意义。[方法/过程] 运用PAD情感模型构建情感语义空间,以2018年食品安全舆情为例,一方面,将情感词映射至情感语义空间,根据位置判别情感词多维情感强度;另一方面,根据情感语义空间的表现形式划分情感层次,探寻不同情感指向特征。[结果/结论] 多维情感语义空间中,食品安全舆情情感的自我认知层愉悦度较高,表明公众认为自身对食品安全有较好的认知;舆情中社会发展和民生民意空间呈现明显的负向情绪,且网民在表达该类情感时的神经生理激活水平较高,应引起政府高度重视。食品安全舆情中的意见领袖对他人情感有较强的影响力,政府应加强与该领域意见领袖的沟通与引导。     

Examining the relationship between specific negative emotions and the perceived helpfulness of online reviews

This paper extracted discrete emotions from online reviews based on an emotion classification approach, and examined the differential effects of three discrete emotions (anger, fear, sadness) on perceived review helpfulness. We empirically tested the hypotheses by analyzing the “verified purchase” reviews on Amazon.com. The findings of this study extend the previous research by suggesting that product type moderates the effects of emotions on perceived review helpfulness. Anger embedded in a customer review exerts a greater negative impact on perceived review helpfulness for experience goods than for search goods. Fear embedded in a review is identified as an important emotional cue to positively affect the perceived review helpfulness with more persuasive messages. As the level of sadness embedded in a review increases, perceived review helpfulness decreases. These findings contribute to a better understanding of the important role of emotions embedded in reviews on the perceived review helpfulness. This study also provides practical insights related to the presentation of online reviews and gives suggestions for consumers regarding how to select and write a helpful review.     

E-word of mouth sentiment analysis for user behavior studies

Nowadays, online word-of-mouth has an increasing impact on people's views and decisions, which has attracted many people's attention.The classification and sentiment analyse in online consumer reviews have attracted significant research concerns. In this thesis, we propose and implement a new method to study the extraction and classification of online dating services(ODS)’s comments. Different from traditional emotional analysis which mainly focuses on product attribution, we attempted to infer and extract the emotion concept of each emotional reviews by introducing social cognitive theory. In this study, we selected 4,300 comments with extremely negative/positive emotions published on dating websites as a sample, and used three machine learning algorithms to analyze emotions. When testing and comparing the efficiency of user's behavior research, we use various sentiment analysis, machine learning techniques and dictionary-based sentiment analysis. We found that the combination of machine learning and lexicon-based method can achieve higher accuracy than any type of sentiment analysis. This research will provide a new perspective for the task of user behavior.     

基于产出导向法的高职英语课程关键能力培养——以教学能力比赛为例

文章基于产出导向法,明晰了高职英语课程教学中“关键能力”的内涵,并以教学能力比赛为例,根据单元的主题和内容进行教学设计,拓宽英语学习的选择空间和途径。学生在“输出”→“输入”→“输出+”的过程中进行学习体验、提升学习效果,实现从语言内容的“识记”向职业工作“能力”的转换,通过完成数个小目标,最终实现大的产出目标,实现六种关键能力的培养。     

Studying emotion induced by music through a crowdsourcing game

One of the major reasons why people find music so enjoyable is its emotional impact. Creating emotion-based playlists is a natural way of organizing music. The usability of online music streaming services could be greatly improved by developing emotion-based access methods, and automatic music emotion recognition (MER) is the most quick and feasible way of achieving it. When resorting to music for emotional regulation purposes, users are interested in the MER method to predict their induced, or felt emotion. The progress of MER in this area is impeded by the absence of publicly accessible ground-truth data on musically induced emotion. Also, there is no consensus on the question which emotional model best fits the demands of the users and can provide an unambiguous linguistic framework to describe musical emotions. In this paper we address these problems by creating a sizeable publicly available dataset of 400 musical excerpts from four genres annotated with induced emotion. We collected the data using an online “game with a purpose” Emotify, which attracted a big and varied sample of participants. We employed a nine item domain-specific emotional model GEMS (Geneva Emotional Music Scale). In this paper we analyze the collected data and report agreement of participants on different categories of GEMS. We also analyze influence of extra-musical factors on induced emotion (gender, mood, music preferences). We suggest that modifications in GEMS model are necessary.     

关于学生工作中“学习共同体”现象的思考--以安徽大学新闻传播学院田径队为例

为完成共同的学习目标,由学习者共同构成的学习共同体,在“公共性”、“民主主义”与“卓越性”的思想指引下,通过人际沟通、交流和分享各种学习资源而相互影响、相互促进的学习,能增强团队凝聚力,提高学习者的归属感,促进学习者的全面成长。如何有效组建不同的学习共同体对提高高校学生工作有着非常重要的意义。     

“互联网+”视域下高校学生党建的提升策略探究

随着互联网的发展,在校大学生无论在何地都可随时借助互联网开展学习、交流等各种活动,这为高校的学生党建工作提供了新的机遇。同时,这需要高校主动把握时代的变化,及时调整学生党建工作的内容和方法,适应"互联网+"背景下的新常态。而本研究从四个方面提出在"互联网+"背景下高校如何根据实际情况构建相关策略,探究"互联网+学生党建"工作模式,以提升高校党建工作水平,满足高校学生党建工作的发展需求。     

基于“EST”理论框架的“三度”课程混合式教学创新模式研究

混合式教学作为一种将教学场景、师生情感、生生情谊共同构建于情境下的灵活、及时和持续的线上、线下共同整合的教学模式,已然成为教育信息化2.0行动计划实施过程中的重要抓手。以“EST”理论框架为基础,以“三度”课程建设为目标,通过对EST理论建构以及课程深度、课堂饱和度和学业紧张度的深度挖掘,提出“EST”理论框架的课程“三度”建设混合式教学创新模式模型。     

《谏逐客书》文本解读与教学内容研究

《谏逐客书》堪称我国古代公文的典范之作,其之所以能让秦王收回逐客的政令,原因有三:避重就轻,切入点巧妙,缓和对立矛盾;从历史、现实、未来三个角度论证“逐客之非”与“纳客之是”;抓住秦王的心理特征,顺其逆鳞。文中精巧的构思、多变的论证方法、考究的用词特点、汪洋恣肆的语言、“书”的文体知识以及李斯对人才观的理解,都是语文课堂中值得探讨的重要内容。     

Dual emotion based fake news detection: A deep attention-weight update approach

The proliferation of false information is a growing problem in today's dynamic online environment. This phenomenon requires automated detection of fake news to reduce its harmful effect on society. Even though various methods are used to detect fake news, most methods only consider data-oriented text features; ignoring dual emotion features (publisher emotions and social emotions) and thus lack higher levels of accuracy. This study addresses this issue by utilizing dual emotion features to detect fake news. The study proposes a Deep Normalized Attention-based mechanism for enriched extraction of dual emotion features and an Adaptive Genetic Weight Update-Random Forest (AGWu-RF) for classification. First, the deep normalized attention-based mechanism incorporates BiGRU, which improves feature value by extracting long-range context information to eliminate gradient explosion issues. The genetic weight for the model is adjusted to RF and updated to achieve optimized hyper parameter values ​​that support the classifiers' detection accuracy. The proposed model outperforms baseline methods on standard benchmark metrics in three real-world datasets. It outperforms state-of-the-art approaches by 5%, 11%, and 14% in terms of accuracy, highlighting the significance of dual emotion capabilities and optimizations in improving fake news detection.     

论平衡轮谈话模型在辅导员谈心谈话工作中的运用

辅导员的谈心谈话工作是辅导员工作中的重中之重。随着“00后”一代进入大学校园,辅导员在谈话时面临的问题也与日俱增。主动开展谈心谈话工作可以有效了解学生最新的学习、生活及思想状态。平衡轮谈话模型是一种科学的谈话模型,通过此方式来开展谈话可以高效、全面地分析学生的现状,从而有针对性地开展谈话工作,进而更好地开展教育工作。     

Listen carefully to experts when you classify data: A generic data classification ontology encoded from regulations

Along with the proliferation of big data technology, organizations are involved in an overwhelming data ocean, the huge volume of data makes them at a loss in the face of frequent data breaches due to their failure of efficient data security management. Data classification has become a hot topic as a cornerstone of data protection especially in China in recent years, by categorizing information types and distinguishing protective measures at different classification levels. Both the text and tables of the promulgated data classification-related regulations (for simplicity, laws, regulations, policies, and standards are collectively referred to as “regulations”) contain a wealth of valuable information which can guide the work of data classification. To best assist data practitioners, in this paper, we automatically “grasp” expert experience on how to classify data from the analysis of such regulations. We design a framework, GENONTO, that automatically extracts data classification practices (DCPs), such as information types and their corresponding sensitive levels to construct an information type lexicon as well as to encode a generic ontology on top of 38 real-world regulations promulgated in China. GENONTO employs machine learning techniques and natural language processing (NLP) to parse unstructured text and tables. To our knowledge, GENONTO is the first work that explores critical information like the category and the sensitivity of information types from regulations, and organizes them in a structured form of ontology, characterizing the subsumptive relations between different information types. Our research helps provide a well-defined integrated view across regulations and bridges the gap between what experts say and how data practitioners do.     

Ontology-based affective models to organize artworks in the social semantic web

In this paper, we focus on applying sentiment analysis to resources from online art collections, by exploiting, as information source, tags intended as textual traces that visitors leave to comment artworks on social platforms. We present a framework where methods and tools from a set of disciplines, ranging from Semantic and Social Web to Natural Language Processing, provide us the building blocks for creating a semantic social space to organize artworks according to an ontology of emotions. The ontology is inspired by the Plutchik’s circumplex model, a well-founded psychological model of human emotions. Users can be involved in the creation of the emotional space, through a graphical interactive interface. The development of such semantic space enables new ways of accessing and exploring art collections.The affective categorization model and the emotion detection output are encoded into W3C ontology languages. This gives us the twofold advantage to enable tractable reasoning on detected emotions and related artworks, and to foster the interoperability and integration of tools developed in the Semantic Web and Linked Data community. The proposal has been evaluated against a real-word case study, a dataset of tagged multimedia artworks from the ArsMeteo Italian online collection, and validated through a user study.     

Fine-grained citation count prediction via a transformer-based model with among-attention mechanism

Previous studies have confirmed that citation mention and location reveal different contributions of the cited articles, and that both are significant in scientific research evaluation. However, traditional citation count prediction only focuses on predicting citation frequency. In this paper, we propose a novel fine-grained citation count prediction task (FGCCP), which aims to predict in-text citation count from each structural function of a paper separately. Specifically, we treated this task as a “sequence to sequence” issue and a multi-task learning job, in which both the inputs and the outputs are based on the sequence pattern of citations from different structural functions. To fulfill FGCCP, we proposed a transformer-based model (i.e. MTAT) in which a novel among-attention mechanism is employed. Based on an empirical study of full-text documents from PubMed Central Open Access Subset, our model achieves satisfactory prediction accuracy, and surpasses common machine learning and deep learning models on FGCCP. Moreover, we also discuss the potential role of the among-attention mechanism and the reason why our proposed model outperforms state-of-the-art strategies. FGCCP may provide more detailed decision-making evidence and evaluation basis for researchers in scientific research evaluation. In addition, MTAT is a general model which can be easily deployed in other multi-task learning jobs.     

The electrical breakdown of liquid dielectrics

Throughout this paper data have been presented showing that the apparent inconsistency of the reported dielectric strength behavior of insulating liquids can be satisfactorily correlated if proper consideration be given to the state of the “purity” of the liquid itself. As a result it is suggested that insulating liquids should be classified as (a) “pure,” indicating those liquids free from dissolved gases as primary “impurities”; and (b) “impure,” including those liquids which contain dissolved gas. The breakdown mechanism depends on the distinctive behavior of these two general classes. “Pure” liquid breakdown is a function of charged particle formation. In part, this may be caused by the assumption of a charge by molecular aggregates, colloidal-like in nature. In part, the charge may arise from molecular ionization by collision. The latter occurs chiefly in the voltage range immediately preceding electrical rupture and is the chief cause of “pure” liquid insulation failure. The presence of the first type of charge—that is, the existence of a difference of potential between molecular aggregates and the liquid—is chiefly responsible for the variation in the time factor to breakdown.The breakdown of “impure” liquids is a function of dissolved gas elimination. This dissolved gas is eliminated as a result of changing solubility produced (a) by electro-striction effects, or (b) by changing pressure or temperature. The presence of secondary impurities such as dust particles and fibers, acts chiefly through the effect on increasing gassing tendencies.It is suggested further that the localization of dielectric breakdown in liquids, irrespective of the type or degree of “purity,” is chiefly in the “neutral membrane” located near the electrodes and formed by the discharge of particles. Such a “neutral membrane” results in a space charge effect giving marked drop in potential and as a result promoting ionization by collision effects in “pure” liquids and electro-striction effects in “impure” liquids.