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《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
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《Information processing & management》2019,56(6):102097
Social media users are increasingly using both images and text to express their opinions and share their experiences, instead of only using text in the conventional social media. Consequently, the conventional text-based sentiment analysis has evolved into more complicated studies of multimodal sentiment analysis. To tackle the challenge of how to effectively exploit the information from both visual content and textual content from image-text posts, this paper proposes a new image-text consistency driven multimodal sentiment analysis approach. The proposed approach explores the correlation between the image and the text, followed by a multimodal adaptive sentiment analysis method. To be more specific, the mid-level visual features extracted by the conventional SentiBank approach are used to represent visual concepts, with the integration of other features, including textual, visual and social features, to develop a machine learning sentiment analysis approach. Extensive experiments are conducted to demonstrate the superior performance of the proposed approach. 相似文献
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【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
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In this paper we present novel ensemble classifier architectures and investigate their influence for offline cursive character recognition. Cursive characters are represented by feature sets that portray different aspects of character images for recognition purposes. The recognition accuracy can be improved by training ensemble of classifiers on the feature sets. Given the feature sets and the base classifiers, we have developed multiple ensemble classifier compositions under four architectures. The first three architectures are based on the use of multiple feature sets whereas the fourth architecture is based on the use of a unique feature set. Type-1 architecture is composed of homogeneous base classifiers and Type-2 architecture is constructed using heterogeneous base classifiers. Type-3 architecture is based on hierarchical fusion of decisions. In Type-4 architecture a unique feature set is learned by a set of homogeneous base classifiers with different learning parameters. The experimental results demonstrate that the recognition accuracy achieved using the proposed ensemble classifier (with best composition of base classifiers and feature sets) is better than the existing recognition accuracies for offline cursive character recognition. 相似文献
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【目的/意义】本文融合文本和图像的多模态信息进行情感识别,引入图片模态信息进行情感语义增强,旨在
解决单一文本模态信息无法准确判定情感极性的问题。【方法/过程】本文以网民在新浪微博发表的微博数据为实
验对象,提出了一种基于DR-Transformer模型的多模态情感识别算法,使用预训练的DenseNet和RoBERTa模型,
分别提取图片模态和文本模态的情感特征;通过引入Modal Embedding机制,达到标识不同模态特征来源的目的;
采用浅层Transformer Encoder对不同模态的情感特征进行融合,利用Self-Attention机制动态调整各模态信息特征
的权重。【结果/结论】在微博数据集上的实验表明：模型情感识别准确率为 79.84%;相较于基于单一文本、图片模
态的情感分类算法,本模型准确率分别提升了 4.74%、19.05%;相较于对不同模态特征向量进行直接拼接的特征融
合方法,本模型准确率提升了 1.12%。充分说明了本模型在情感识别的问题上具有科学性、合理性、有效性。【创
新/局限】利用 Modal Embedding 和 Self-Attention 机制能够有效的融合多模态信息。微博网络舆情数据集还需进
一步扩充。 相似文献
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《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
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《Information processing & management》2022,59(2):102872
In an environment full of disordered information, the media spreads fake or harmful information into the public arena with a speed which is faster than ever before. A news report should ideally be neutral and factual. Excessive personal emotions or viewpoints should not be included. News articles ought not to be intentionally or maliciously written or create a media framing. A harmful news is defined as those explicit or implicit harmful speech in news text that harms people or affects readers’ perception. However, in the current situation, it is difficult to effectively identify and predict fake or harmful news in advance, especially harmful news. Therefore, in this study, we propose a Bidirectional Encoder Representation from Transformers (BERT) based model which applies ensemble learning methods with a text sentiment analysis to identify harmful news, aiming to provide readers with a way to identify harmful news content so as to help them to judge whether the information provided is in a more neutral manner. The working model of the proposed system has two phases. The first phase is collecting harmful news and establishing a development model for analyzing the correlation between text sentiment and harmful news. The second phase is identifying harmful news by analyzing text sentiment with an ensemble learning technique and the BERT model. The purpose is to determine whether the news has harmful intentions. Our experimental results show that the F1-score of the proposed model reaches 66.3%, an increase of 7.8% compared with that of the previous term frequency-inverse document frequency approach which adopts a Lagrangian Support Vector Machine (LSVM) model without using a text sentiment. Moreover, the proposed method achieves a better performance in recognizing various cases of information disorder. 相似文献
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【目的/意义】随着社交网络的复杂化,当前谣言往往是由描述事件的文本、对应的图片或者视频组成,多种
模态的谣言更容易给用户传达一种错误的认知。现有谣言检测的研究往往只使用谣言文本特征,且未能充分挖掘
谣言与事件存在的联系。【方法/过程】因此本文提出一种基于增强对抗网络和多模态融合的谣言检测方法,使用
BERT 和 Text-CNN 提取文本特征,使用 VGG-19网络提取图像特征,再通过注意力机制捕捉多个模态的特征交
互,最后使用增强对抗网络来挖掘谣言和事件之间联系。【结果/结论】在公开的微博多模态数据集上进行对比实
验,实验结果表明该方法检测的准确率达到了 92.5%,相较于传统单模态和现有多模态模型,提升了约 10%~20%。
【创新/局限】本文将对抗网络和多模态特征融入谣言检测中,有效提升了谣言检测的效果,但目前仅尝试了文本和
图像两种模态的结合,如何融合更多模态的特征后续有待研究。 相似文献
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《Information processing & management》2023,60(1):103120
Multimodal fake news detection methods based on semantic information have achieved great success. However, these methods only exploit the deep features of multimodal information, which leads to a large loss of valid information at the shallow level. To address this problem, we propose a progressive fusion network (MPFN) for multimodal disinformation detection, which captures the representational information of each modality at different levels and achieves fusion between modalities at the same level and at different levels by means of a mixer to establish a strong connection between the modalities. Specifically, we use a transformer structure, which is effective in computer vision tasks, as a visual feature extractor to gradually sample features at different levels and combine features obtained from a text feature extractor and image frequency domain information at different levels for fine-grained modeling. In addition, we design a feature fusion approach to better establish connections between modalities, which can further improve the performance and thus surpass other network structures in the literature. We conducted extensive experiments on two real datasets, Weibo and Twitter, where our method achieved 83.3% accuracy on the Twitter dataset, which has increased by at least 4.3% compared to other state-of-the-art methods. This demonstrates the effectiveness of MPFN for identifying fake news, and the method reaches a relatively advanced level by combining different levels of information from each modality and a powerful modality fusion method. 相似文献
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《Information processing & management》2023,60(3):103243
The detection and identification of traffic signs is a fundamental function of an intelligent transportation system. The extraction or identification of a road sign poses the same problems as object identification in natural contexts: conditions of illumination are variable and uncontrollable, and various objects frequently surround road signs. These difficulties make the extraction of features difficult. The fusion of time and space features of traffic signs is important for improving the performance of sign recognition. Deep learning-based algorithms are time-consuming to train based on a large amount of data. They are difficult to deploy on resource-constrained portable devices and conduct sign detection in real time. The accuracy of sign detection should be further improved, which is related to the safety of traffic participants. To improve the accuracy of feature extraction and classification of traffic signs, we propose MKL-SING, a hybrid approach based on multi-kernel support vector machine (MKL-SVM) for public transportation SIGN recognition. It contains three main components: a principal component analysis for image dimension reduction, a fused feature extractor, and a multi-kernel SVM-based classifier. The fused feature extractor extracts and fuses the time and space features of traffic signs. The multi-kernel SVM then classifies the traffic signs based on the fused features. Different kernel functions in the multi-kernel SVM are fused based on a feature weighting procedure. Compared with single-core SVM, multi-kernel SVM can better process massive data because it can project each kernel function into high-dimensional feature space to get global solutions. Finally, the performance of SVM-TSR is validated based on three traffic sign datasets. Experiment results show that SVM-TSR performs better than state-of-the-art methods in terms of dynamic traffic sign identification and recognition. 相似文献
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【目的/意义】随着社交媒体的发展,各类新闻数量激增,舆情监测处理越来越重要,高效精确的识别舆情新
闻可以帮助有关部门及时搜集跟踪突发事件信息并处理,减小舆论对社会的影响。本文提出一种融合 BERT、
TEXTCNN、BILSTM的新闻标题文本分类模型,充分考虑词嵌入信息、文本特征和上下文信息,以提高新闻标题类
别识别的准确率。【方法/过程】将使用BERT生成的新闻标题文本向量输入到TEXTCNN提取特征,将TEXTCNN
的结果输入到 BILSTM 捕获新闻标题上下文信息,利用 softmax判断分类结果。【结果/结论】研究表明,本文提出的
融合了基于语言模型的 BERT、基于词向量 TEXTCNN 和基于上下文机制 BILSTM 三种算法的分类模型在准确
率、精确率、召回率和F1值均达到了0.92以上,而且具有良好的泛化能力,优于传统的文本分类模型。【创新/局限】
本文使用BERT进行词嵌入,同时进行特征提取和捕获上下文语义,模型识别新闻类别表现良好,但模型参数较多
向量维度较大对训练设备要求较高,同时数据类别只有10类,未对类别更多或类别更细化的数据进行实验。 相似文献
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【 目的/意义】开展面向领域的细粒度命名实体识别研究对于提升文本挖掘精度具有重要的意义,本文以番
茄病虫害命名实体为例,探索采用深度学习技术实现面向领域的细粒度命名实体识别研究方法。【目的/意义】文章
以电子书、论文、网页作为数据源,选择品种、病虫害、症状、时间、部位、防治药剂六类实体进行标注,利用BERT和
CBOW 预训练字向量分别输入 BiLSTM-CRF 模型训练,并在识别后补充规则控制实体的边界。【结果/结论】
BERT预训练的字向量和BiLSTM-CRF结合,在补充规则控制后F值达到了81.03%,优于其它模型,在番茄病虫害
领域的实体识别中具有较好的效果。【创新/局限】BERT预训练的字向量可以有效降低番茄病虫害领域实体因分
词错误带来的影响,针对不同实体的特点,补充规则可以有效控制实体边界,提高识别准确率。但本文的规则补充
仅在测试阶段,并没有加入训练过程,整体的准确率还有待提高。 相似文献
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【目的/意义】探究针对微博文本的基于深度学习的情绪分类有效方法,研究微博热点事件下用户转发言论的情绪类型与隐私信息传播的关系。【方法/过程】选用BERT、BERT+CNN、BERT+RNN和ERNIE四个深度学习分类模型设置对比实验,在重新构建情绪7分类语料库的基础上验证性能较好的模型。选取4个微博热点案例,从情绪分布、情感词词频、转发时间和转发次数四个方面展开实证分析。【结果/结论】通过实证研究发现,用户在传播隐私信息是急速且短暂的,传播时以“愤怒”和“厌恶”等为代表的消极情绪占主导地位,且会因隐私信息主体的不同而产生情绪类型和表达方式上的差异。【创新/局限】研究了用户在传播隐私信息行为时的情绪特征及二者的联系,为保护社交网络用户隐私信息安全提供有价值的理论和现实依据,但所构建的语料库数据量对于训练一个高准确率的深度学习模型而言还不够,且模型对于反话、反讽等文本的识别效果不佳。 相似文献
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[目的/意义]准确把握公众微博评论中所反映的公众观点并总结舆论焦点,有助于及时获取和引导社会舆情态势,对政府公信力、快速响应能力及执行力提升具有支撑作用。[方法/过程]文章针对当前政府微博评论社会功能发挥的现实要求和其文本特征挖掘的技术需求,从基于深度学习的文本智能语义理解和挖掘出发,提出了适用的细粒度四元组标注策略,构建了政府微博评论观点抽取与焦点呈现的深度学习模型POF-BiLSTM-CRF,即通过细粒度标注策略确定、Word2vec训练词向量、BiLSTM评论特征学习进行标签及其概率输出、CRF学习上下文实现微博评论标注优化,以及观点聚类和主题词提取后最终呈现舆论焦点。[结果/结论]针对"中国警方在线"微博评论的实验表明,文章所提研究框架和模型能够有效进行舆论观点的智能化提取,为快速把握公众观点及为政府决策提供了参考。 相似文献
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《Information processing & management》2023,60(1):103154
Image–text matching is a crucial branch in multimedia retrieval which relies on learning inter-modal correspondences. Most existing methods focus on global or local correspondence and fail to explore fine-grained global–local alignment. Moreover, the issue of how to infer more accurate similarity scores remains unresolved. In this study, we propose a novel unifying knowledge iterative dissemination and relational reconstruction (KIDRR) network for image–text matching. Particularly, the knowledge graph iterative dissemination module is designed to iteratively broadcast global semantic knowledge, enabling relevant nodes to be associated, resulting in fine-grained intra-modal correlations and features. Hence, vector-based similarity representations are learned from multiple perspectives to model multi-level alignments comprehensively. The relation graph reconstruction module is further developed to enhance cross-modal correspondences by constructing similarity relation graphs and adaptively reconstructing them. We conducted experiments on the datasets Flickr30K and MSCOCO, which have 31,783 and 123,287 images, respectively. Experiments show that KIDRR achieves improvements of nearly 2.2% and 1.6% relative to Recall@1 on Flicr30K and MSCOCO, respectively, compared to the current state-of-the-art baselines. 相似文献
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《Information processing & management》2022,59(5):103048
Existing methods for text generation usually fed the overall sentiment polarity of a product as an input into the seq2seq model to generate a relatively fluent review. However, these methods cannot express more fine-grained sentiment polarity. Although some studies attempt to generate aspect-level sentiment controllable reviews, the personalized attribute of reviews would be ignored. In this paper, a hierarchical template-transformer model is proposed for personalized fine-grained sentiment controllable generation, which aims to generate aspect-level sentiment controllable reviews with personalized information. The hierarchical structure can effectively learn sentiment information and lexical information separately. The template transformer uses a part of speech (POS) template to guide the generation process and generate a smoother review. To verify our model, we used the existing model to obtain a corpus named FSCG-80 from Yelp, which contains 800K samples and conducted a series of experiments on this corpus. Experimental results show that our model can achieve up to 89.93% aspect-sentiment control accuracy and generate more fluent reviews. 相似文献
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文章设计一种部署于互联网上的舆情监测应用系统,该系统能够对互联网上的网页、论坛、微博等多种信息媒介进行监测,并自动的采集各种页面上的数据。通过数据挖掘方法从互联网中不同的信息媒介中提取出有价值的信息,并对网络舆情的状况和发展趋势做出预测,实现对互联网上的网络舆情进行实时监测的应用目标。 相似文献