共查询到20条相似文献，搜索用时 187 毫秒
1.
2.
3.
在教育技术学的研究中,始终把传播学作为重要的理论基础加以援引,并逐渐形成了对教育传播的专门研究。已有的研究主要关注的是教育传播的过程模式,并利用现代媒体手段更全面便捷地实现教育传播过程的各个环节。本文由实践需要出发,转而关注教育传播的意义和价值实现,引入了传播的符号研究论述,从符号学的视角分析了教育传播的意义产生。 相似文献
4.
5.
6.
在雅各布森的符指理论中,广告制作和传播过程可以被看作是一个符指过程,涉及到符号学领域的六个因素:发送者、能指、所指、中介、符码和接受者。在符号传递的过程中,符指过程中的六个因素地位不是平等的,会出现某种倾向。根据这些不平等在广告语言中的体现,笔者将广告文本划分为六种类型,并对其进行了符号学视角的阐释;同时在分析又指出了符指理论在广告语言分析时的不足之处。 相似文献
7.
信息系统的建设须从正确理解并建立组织模型开始,从符号学的观点出发,组织中信息处理和交流都是通过创建、传递和使用符号来完成的。通过介绍符号学理论基础、符号学为基础的组织观点、组织符号学的框架,对组织符号学建模时的重要概念,以符号学为基础的组织建模方法进行了分析,指出组织符号学建模时存在的问题及未来的研究方向。 相似文献
8.
互文性强调特定文本与其它相关文本之间的相互影响、相互联系,是理解一切文本的前提。根据互文性理论。任何一个文本都是在它以前的文本的遗迹或记忆的基础上产生的,或者是对其他文本的吸收和转化中形成的。本文将这一概念应用到广告翻译中、通过分析广告语中的互文性,及广告译文的研究,为广告翻译研究开拓了新的视角。 相似文献
9.
符号学认为语言意义包括指称意义、言内意义和语用意义。本文尝试探索这三个意义与奈达翻译理论的关系,提炼奈达翻译理论中的符号学概念,力求更深刻理解奈达翻译理论,对翻译实践有一定的指导作用。 相似文献
10.
传统的科学文本分析范式是内容无涉的神话式研究模式,它在科学文本与科学实践之间作了“真实反映论”的处理。而SSK学者则打开了科学文本的黑箱,从认知的角度来解构传统的科学文本神话观,试图打破传统科学文本观在文本与真实世界之间所标注的反映论逻辑。文章认为这种转向既与科学哲学界语言学、修辞学、解释学转向相一致,同时也打开了SSK进行自我反思的通途,具有重要的学术意义;就实践意义来讲,它提醒人们进行科学文本关注,为科学失范的预防提供认识论依据,同时也启示了一种基于传统宏观分析与SSK微观研究互补的新的方法论。 相似文献
11.
运用数据挖掘技术得到旅游文本属性与特征已成为旅游研究的重要领域,对旅游微博发文主题的研究有助于旅游机构形象塑造及内容传播推广,对旅游机构的微博信息供给及旅游形象的提升具有一定意义。本研究首先对内容分析法在旅游研究的运用状况及国内外旅游微博相关方面的研究进行梳理;其次,以国家旅游局新浪微博的网络文本内容为研究对象,借助Rost word parser词频分析软件提取网络文本的高频特征词并进行筛选;再次,采用内容分析法,结合社会网络及共词分析法,得到网络文本高频词之间的社会网络联系;最后,探索高频词的属性及其之间的联系特征,在国家旅游局新浪微博的内容分析基础上,提炼出其微博内容分为人文景观、自然景观、游客出行、旅游政务信息4个主题。 相似文献
12.
《Information processing & management》2023,60(2):103229
Multimodal sentiment analysis aims to judge the sentiment of multimodal data uploaded by the Internet users on various social media platforms. On one hand, existing studies focus on the fusion mechanism of multimodal data such as text, audio and visual, but ignore the similarity of text and audio, text and visual, and the heterogeneity of audio and visual, resulting in deviation of sentiment analysis. On the other hand, multimodal data brings noise irrelevant to sentiment analysis, which affects the effectness of fusion. In this paper, we propose a Polar-Vector and Strength-Vector mixer model called PS-Mixer, which is based on MLP-Mixer, to achieve better communication between different modal data for multimodal sentiment analysis. Specifically, we design a Polar-Vector (PV) and a Strength-Vector (SV) for judging the polar and strength of sentiment separately. PV is obtained from the communication of text and visual features to decide the sentiment that is positive, negative, or neutral sentiment. SV is gained from the communication between the text and audio features to analyze the sentiment strength in the range of 0 to 3. Furthermore, we devise an MLP-Communication module (MLP-C) composed of several fully connected layers and activation functions to make the different modal features fully interact in both the horizontal and the vertical directions, which is a novel attempt to use MLP for multimodal information communication. Finally, we mix PV and SV to obtain a fusion vector to judge the sentiment state. The proposed PS-Mixer is tested on two publicly available datasets, CMU-MOSEI and CMU-MOSI, which achieves the state-of-the-art (SOTA) performance on CMU-MOSEI compared with baseline methods. The codes are available at: https://github.com/metaphysicser/PS-Mixer. 相似文献
13.
[目的/意义]网民对突发事件的评论和传播会造成涉事组织的网络舆情危机,影响组织的声誉和形象。而网民的情绪化传播是引发网络舆情危机的重要因素,但目前缺乏对网民情绪化传播机理的研究。[方法/过程]使用扎根理论研究方法,以D&G辱华事件为对象,以网民对事件的评论内容为样本进行研究。[结果/结论]通过三级编码归纳出情绪化传播的形成要素,构建了网络舆情情绪化传播的机理模型。 相似文献
14.
图像中的文安信息在一定程度上反映了图像主要内容，因此对于图像中的文字进行检测是非常有意义的。本文采用了基于字符的纹理特征的方法，在DCT域中对字符进行检测，从而将图像中的字符区域检测出来，最后用matlab进行了验证，实验表明该方法能够取得较好结果。 相似文献
15.
【目的/意义】社交媒体在改变新闻传播以及人类获取信息方式的同时,也成为了虚假新闻传播的主要渠
道。因此,快速识别社交媒体中的虚假新闻,扼制虚假信息的传播,对净化网络空间、维护公共安全至关重要。【方
法/过程】为了有效识别社交媒体上发布的虚假新闻,本文基于对虚假新闻内容特征的深入剖析,分别设计了文本
词向量、文本情感、图像底层、图像语义特征的表示方法,用以提取社交网络中虚假新闻的图像特征信息和文本特
征信息,构建多模态特征融合的虚假新闻检测模型,并使用MediaEval2015数据集对模型性能进行效果验证。【结果/
结论】通过对比分析不同特征组合方式和不同分类方法的实验结果,发现融合文本特征和图像特征的多模态模型
可以有效提升虚假新闻检测效果。【创新/局限】研究从多模态的角度设计了虚假新闻检测模型,融合了文本与图像
的多种特征。然而采用向量拼接来实现特征融合,不仅无法实现各种特征的充分互补,而且容易造成维度灾难。 相似文献
16.
17.
Communication is considered to be one of the most essential components of collaboration, but our understanding as to which form of communication provides the most optimal cost-benefit balance lacks severely. To help investigate effects of various communication channels on a collaborative project, we conducted a user study with 30 pairs (60 participants) in three different conditions – co-located, remotely located with text chat, and remotely located with text as well as audio chat, in an exploratory search task. Using both quantitative and qualitative data analysis, we found that teams with remotely located participants were more effective in terms of being able to explore more diverse information. Adding audio support for remote collaboration helped participants to lower their cognitive load as well as negative emotions compared to those working in the same space. We also show how these findings could help design more effective systems for collaborative information seeking tasks using adequate and appropriate communication. We argue that collaboration is an important aspect of human-centered IR, and that our work provides interesting insights into people doing information seeking/retrieval in collaboration. 相似文献
18.
19.
《Information processing & management》2020,57(1):102141
Detecting sentiments in natural language is tricky even for humans, making its automated detection more complicated. This research proffers a hybrid deep learning model for fine-grained sentiment prediction in real-time multimodal data. It reinforces the strengths of deep learning nets in combination to machine learning to deal with two specific semiotic systems, namely the textual (written text) and visual (still images) and their combination within the online content using decision level multimodal fusion. The proposed contextual ConvNet-SVMBoVW model, has four modules, namely, the discretization, text analytics, image analytics, and decision module. The input to the model is multimodal text, m ε {text, image, info-graphic}. The discretization module uses Google Lens to separate the text from the image, which is then processed as discrete entities and sent to the respective text analytics and image analytics modules. Text analytics module determines the sentiment using a hybrid of a convolution neural network (ConvNet) enriched with the contextual semantics of SentiCircle. An aggregation scheme is introduced to compute the hybrid polarity. A support vector machine (SVM) classifier trained using bag-of-visual-words (BoVW) for predicting the visual content sentiment. A Boolean decision module with a logical OR operation is augmented to the architecture which validates and categorizes the output on the basis of five fine-grained sentiment categories (truth values), namely ‘highly positive,’ ‘positive,’ ‘neutral,’ ‘negative’ and ‘highly negative.’ The accuracy achieved by the proposed model is nearly 91% which is an improvement over the accuracy obtained by the text and image modules individually. 相似文献
20.
研究文本定位与特征提取问题。针对传统的Canny算子图像检测算法的不足,提出了一种改进的Canny算子图像信息特征提取算法。研究方法是:首先对彩色图像进行高斯金字塔分解,然后用Canny算子检测彩色图像,提取边缘图像,再经过通二值化,去噪方差投影定位文本区域。实验结果表明,本文提出的方法有效、实用。 相似文献