共查询到20条相似文献，搜索用时 198 毫秒
1.
以公共事件为切入点,从绿色创新角度出发,将从基于技术创新的企业社会责任对企业财务绩效影响这条意识的路径来研究,而不是基于社会责任的创新对企业财务绩效的影响。从大量文献研究发现,创新的绿色也就是基于技术创新的社会责任,我们将剖析基于技术创新尤其是产品和工艺创新的企业社会责任对于企业社会绩效和财务绩效的影响机制和媒体曝光度在这种影响机制中所起到的调节作用。研究认为,基于技术创新的企业社会责任会积极影响企业社会绩效,社会绩效会积极影响企业财务绩效,而媒体曝光度则会正向调节基于技术创新的企业社会责任对社会绩效影响机制。 相似文献
2.
《Information processing & management》2023,60(4):103407
Identifying the emotional causes of mental illnesses is key to effective intervention. Existing emotion-cause analysis approaches can effectively detect simple emotion-cause expressions where only one cause and one emotion exist. However, emotions may often result from multiple causes, implicitly or explicitly, with complex interactions among these causes. Moreover, the same causes may result in multiple emotions. How to model the complex interactions between multiple emotion spans and cause spans remains under-explored. To tackle this problem, a contrastive learning-based framework is presented to detect the complex emotion-cause pairs with the introduction of negative samples and positive samples. Additionally, we developed a large-scale emotion-cause dataset with complex emotion-cause instances based on subreddits associated with mental health. Our proposed approach was compared to prevailing CNN-based, LSTM-based, Transformer-based and GNN-based methods. Extensive experiments have been conducted and the quantifiable outcomes indicate that our proposed solution achieves competitive performance on simple emotion-cause pairs and significantly outperformed baseline methods in extracting complex emotion-cause pairs. Empirical studies further demonstrated that our proposed approach can be used to reveal the emotional causes of mental disorders for effective intervention. 相似文献
3.
《Information processing & management》2023,60(3):103305
Social emotion refers to the emotion evoked to the reader by a textual document. In contrast to the emotion cause extraction task which analyzes the cause of the author's sentiments based on the expressions in text, identifying the causes of social emotion evoked to the reader from text has not been explored previously. Social emotion mining and its cause analysis is not only an important research topic in Web-based social media analytics and text mining but also has a number of applications in multiple domains. As the focus of social emotion cause identification is on analyzing the causes of the reader's emotions elicited by a text that are not explicitly or implicitly expressed, it is a challenging task fundamentally different from the previous research. To tackle this, it also needs a deeper level understanding of the cognitive process underlying the inference of social emotion and its cause analysis. In this paper, we propose the new task of social emotion cause identification (SECI). Inspired by the cognitive structure of emotions (OCC) theory, we present a Cognitive Emotion model Enhanced Sequential (CogEES) method for SECI. Specifically, based on the implications of the OCC model, our method first establishes the correspondence between words/phrases in text and emotional dimensions identified in OCC and builds the emotional dimension lexicons with 1,676 distinct words/phrases. Then, our method utilizes lexicons information and discourse coherence for the semantic segmentation of document and the enhancement of clause representation learning. Finally, our method combines text segmentation and clause representation into a sequential model for cause clause prediction. We construct the SECI dataset for this new task and conduct experiments to evaluate CogEES. Our method outperforms the baselines and achieves over 10% F1 improvement on average, with better interpretability of the prediction results. 相似文献
4.
【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。 相似文献
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中职语文教材,蕴含了各种情感因素,那么,教师在传授知识的同时,按新课改的教学要求就必须激发学生积极向上的情感,传授人生之道。因此,教师在教学过程中就必须仔细考虑各个教学环节,运用各种各样的教学方法,融情于教,唤起学生的情感,培养学生的积极情感。 相似文献
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以被深圳社会责任指数收录的61家制造业公司为样本,将其定义为社会责任企业,未被收录的深市制造业公司定义为非社会责任企业,并采用倾向分数配对方法,控制公司规模、财务状况、管理能力及公司治理等特征变量的影响,进而研究企业社会责任行为是否可以改善财务绩效的问题。研究结果表明,社会责任企业的财务绩效显著优于非社会责任企业,企业社会责任行为可以改善财务绩效。 相似文献
7.
《Information processing & management》2023,60(4):103354
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. 相似文献
8.
选取我国食品行业38家上市公司2015—2017年面板数据为样本,采用随机效应模型方法,实证分析社会责任对公司财务绩效影响。研究表明:食品行业企业社会责任能够给公司财务绩效带来显著影响。根据利益相关者理论将社会责任划分为六个维度,发现企业对债权人的社会责任能够对财务绩效带来显著正向作用,企业对消费者和员工的社会责任对财务绩效带来显著负向作用;进一步区分产权异质性,发现与非国有企业相比,社会责任履行对财务绩效带来显著影响在国有企业中表现更为明显。经过稳健性检验发现上述结论依然成立,说明我国食品行业社会责任整体状况有待提高。 相似文献
9.
《Information processing & management》2020,57(2):102147
The combination of large open data sources with machine learning approaches presents a potentially powerful way to predict events such as protest or social unrest. However, accounting for uncertainty in such models, particularly when using diverse, unstructured datasets such as social media, is essential to guarantee the appropriate use of such methods. Here we develop a Bayesian method for predicting social unrest events in Australia using social media data. This method uses machine learning methods to classify individual postings to social media as being relevant, and an empirical Bayesian approach to calculate posterior event probabilities. We use the method to predict events in Australian cities over a period in 2017/18. 相似文献
10.
《Information processing & management》2020,57(3):102087
With the rapid development in mobile computing and Web technologies, online hate speech has been increasingly spread in social network platforms since it's easy to post any opinions. Previous studies confirm that exposure to online hate speech has serious offline consequences to historically deprived communities. Thus, research on automated hate speech detection has attracted much attention. However, the role of social networks in identifying hate-related vulnerable community is not well investigated. Hate speech can affect all population groups, but some are more vulnerable to its impact than others. For example, for ethnic groups whose languages have few computational resources, it is a challenge to automatically collect and process online texts, not to mention automatic hate speech detection on social media. In this paper, we propose a hate speech detection approach to identify hatred against vulnerable minority groups on social media. Firstly, in Spark distributed processing framework, posts are automatically collected and pre-processed, and features are extracted using word n-grams and word embedding techniques such as Word2Vec. Secondly, deep learning algorithms for classification such as Gated Recurrent Unit (GRU), a variety of Recurrent Neural Networks (RNNs), are used for hate speech detection. Finally, hate words are clustered with methods such as Word2Vec to predict the potential target ethnic group for hatred. In our experiments, we use Amharic language in Ethiopia as an example. Since there was no publicly available dataset for Amharic texts, we crawled Facebook pages to prepare the corpus. Since data annotation could be biased by culture, we recruit annotators from different cultural backgrounds and achieved better inter-annotator agreement. In our experimental results, feature extraction using word embedding techniques such as Word2Vec performs better in both classical and deep learning-based classification algorithms for hate speech detection, among which GRU achieves the best result. Our proposed approach can successfully identify the Tigre ethnic group as the highly vulnerable community in terms of hatred compared with Amhara and Oromo. As a result, hatred vulnerable group identification is vital to protect them by applying automatic hate speech detection model to remove contents that aggravate psychological harm and physical conflicts. This can also encourage the way towards the development of policies, strategies, and tools to empower and protect vulnerable communities. 相似文献
11.
《Information processing & management》2022,59(6):103098
The digital currency has taken the financial markets by storm ever since its inception. Academia and industry are focussing on Artificial intelligence (AI) tools and techniques to study and gain an understanding of how businesses can draw insights from the large-scale data available online. As the market is driven by public opinions, and social media today provides an encouraging platform to share ideas and views; organizations and policy-makers could use the natural language processing (NLP) technology of AI to analyze public sentiments. Recently, a new and moderately unconventional instrument known as non-fungible tokens (NFTs) is emerging as an upcoming business market. Unlike the stock market, no precise quantitative parameters exist for the price determination of NFTs. Instead, NFT markets are driven more by public opinion, expectations, the perception of buyers, and the goodwill of creators. This study evaluates human emotions on the social media platforms Twitter posted by the public relating to NFTs. Additionally, this study conducts secondary market analysis to determine the reasons for the growing acceptance of NFTs through sentiment and emotion analysis. We segregate tweets using Pearson Product-Moment Correlation Coefficient (PPMCC) and study 8-scale emotions (Anger, Anticipation, Disgust, Fear, Joy, Sadness, Surprise, and Trust) along with Positive and Negative sentiments. Tweets majorly contained positive sentiment (～ 72%), and positive emotions like anticipation and trust were found to be predominant all over the world. This is the first of its kind financial and emotional analysis of tweets pertaining to NFTs to the best of our understanding. 相似文献
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本文以交通运输行业企业社会责任履行对企业财务绩效的影响为立足点,采用利益相关者理论进行多维度社会责任指标划分,通过静态回归分析、动态滞后模型对2011-2015年期间在沪深证券交易所上市的37家交通运输行业公司进行实证研究并提出对策建议。研究结果表明：交通运输行业企业社会责任的履行及企业的成长性均对当期财务绩效指标存在明显的正向作用,而公司的规模对当期财务绩效有明显的负向影响。财务绩效将同时受到滞后一期及滞后两期企业社会责任的正向影响,且时间越长,当期社会责任对后期财务绩效的影响越弱。无论对于当期或后期财务绩效指标而言,企业社会责任均对会计指标衡量的财务绩效产生较市场指标衡量更为显著的正向作用。 相似文献
15.
《Information processing & management》2023,60(2):103239
Exploring the factors that affect the market performance of paid knowledge products is of great importance for knowledge payment platforms. Drawing on the sensations-familiarity framework and social capital theory, this study investigates how knowledge differentiation between paid and free knowledge impacts market performance, along with the moderating effect of knowledge providers’ social capital. Technically, a neural network-based text mining model is utilized to transform free and paid knowledge to semantic vectors, whose dissimilarity is calculated as knowledge differentiation. Empirical analysis on a real dataset reveals the positive (or negative) effect of knowledge differentiation on sales (or eWOM, electronic word of mouth), which will be more prominent with the increase of social capital. The results are reinforced with robustness checks regarding alternative knowledge-differentiation measures, more control variables and alternative regression methods. The present study extends our understanding of knowledge payment and free-to-paid consumption, and offers practical implications for content design and product management. 相似文献
16.
《Information processing & management》2023,60(5):103463
Entity linking (EL), the task of automatically matching mentions in text to concepts in a target knowledge base, remains under-explored when it comes to the food domain, despite its many potential applications, e.g., finding the nutritional value of ingredients in databases. In this paper, we describe the creation of new resources supporting the development of EL methods applied to the food domain: the E.Care Knowledge Base (E.Care KB) which contains 664 food concepts and the E.Care dataset, a corpus of 468 cooking recipes where ingredient names have been manually linked to corresponding concepts in the E.Care KB. We developed and evaluated different methods for EL, namely, deep learning-based approaches underpinned by Siamese networks trained under a few-shot learning setting, traditional machine learning-based approaches underpinned by support vector machines (SVMs) and unsupervised approaches based on string matching algorithms. Combining the strengths of each of these approaches, we built a hybrid model for food EL that balances the trade-offs between performance and inference speed. Specifically, our hybrid model obtains 89.40% accuracy and links mentions at an average speed of 0.24 seconds per mention, whereas our best deep learning-based model, SVM model and unsupervised model obtain accuracies of 86.99%, 87.19% and 87.43% at inference speeds of 0.007, 0.66 and 0.02 seconds per mention, respectively. 相似文献
17.
The aim of this study is to propose an automatic and real-time social media analytics framework with interactive data visualizations to support effective exploration of knowledge about adverse drug reaction (ADR) surveillance. This proposed framework has been prototypically implemented on the basis of social media data. A longitudinal diabetes patient online community data (AskaPatient.com) as well as FDA Adverse Event Reporting Systems (FAERS) data as a benchmark were used to evaluate our proposed approach’s performance. Based on the results, our approach significantly increases the precision and accuracy for ADR extraction. The number of ADR cases, the time when the ADRs occurred, and the rating of Glucophage have been visualized that resulted by mining a collection of 870 ADRs posted in Askapatents.com over a certain time period (from 2001 to 2015). The results have important implications for pharmaceutical companies and hospitals wishing to monitor ADRs of medicines. 相似文献
18.
This study examines the literary corpus on the role and potential of blockchain technology in promoting gender equality through the lens of new technology-oriented corporate governance models. It investigates if and how corporate governance models can include blockchain technology to add value to gender equality and inclusion processes, in line with Sustainable Development Goal (SDG) 5. A bibliometric analysis of a database—containing 127 articles, 4 United Nations reports, 3 European institutions’ reports, 1 International Labour Office report, 1 World Economic Forum report, and 4 industry reports useful to our analysis—was conducted from 1990 to 2021, to provide a map of the knowledge generated and circulated by the literature. This study offers insights into publication activities, essential topics, citation trends, and the status of collaborations between contributors to previous research and aggregated contributions to the area of blockchain technology studies. Furthermore, the study offers a retrospective analysis of the content published in the blockchain technology field. The findings indicate that field research has focussed primarily on blockchain’s economic and financial attributes but not on social potential. This study emphasises the implementation of blockchain technology to manage gender equality and inclusion processes by orienting corporate governance models towards social and sustainable values. 相似文献
19.
Gerald Petz Michał Karpowicz Harald Fürschuß Andreas Auinger Václav Stříteský Andreas Holzinger 《Information processing & management》2014
The emerging research area of opinion mining deals with computational methods in order to find, extract and systematically analyze people’s opinions, attitudes and emotions towards certain topics. While providing interesting market research information, the user generated content existing on the Web 2.0 presents numerous challenges regarding systematic analysis, the differences and unique characteristics of the various social media channels being one of them. This article reports on the determination of such particularities, and deduces their impact on text preprocessing and opinion mining algorithms. The effectiveness of different algorithms is evaluated in order to determine their applicability to the various social media channels. Our research shows that text preprocessing algorithms are mandatory for mining opinions on the Web 2.0 and that part of these algorithms are sensitive to errors and mistakes contained in the user generated content. 相似文献
20.
Quickly and accurately summarizing representative opinions is a key step for assessing microblog sentiments. The Ortony-Clore-Collins (OCC) model of emotion can offer a rule-based emotion export mechanism. In this paper, we propose an OCC model and a Convolutional Neural Network (CNN) based opinion summarization method for Chinese microblogging systems. We test the proposed method using real world microblog data. We then compare the accuracy of manual sentiment annotation to the accuracy using our OCC-based sentiment classification rule library. Experimental results from analyzing three real-world microblog datasets demonstrate the efficacy of our proposed method. Our study highlights the potential of combining emotion cognition with deep learning in sentiment analysis of social media data. 相似文献