Multimodality Sentiment Analysis in Social Internet of Things Based on Hierarchical Attentions and CSAT-TCN With MBM Network

Xiao, Guohui; Tu, Geng; Zheng, Lin; Zhou, Teng; Li, Xin; Ahmed, Syed Hassan; Jiang, Dazhi

doi:10.1109/jiot.2020.3015381

Cited by 50 publications

(14 citation statements)

References 25 publications

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“…In this paper, the subjective questions in the test corpus are classified into three major categories by topic type, definition, sequential, and general, and then into two major categories by question type, subjective questions with question words and subjective questions with question prayers. In summary, this paper classifies the subjective questions in the subjective question corpus into six categories, namely, subjective questions with question words in the defined category, subjective questions with question prayer words in the defined category, subjective questions with question words in the sequence category, subjective questions with question prayer words in the sequence category, subjective questions with question words in the general category, and subjective questions with question prayer words in the general category [ 15 ]. For different types of interrogative sentences, different subjective question marking methods were used to realize the adaptive marking of subjective questions in this paper.…”

Section: Analytical Design Of English Writing Neural Network Algorithmmentioning

confidence: 99%

Construction of an Artificial Intelligence Writing Model for English Based on Fusion Neural Network Model

Hsiao

Hung

2022

Computational Intelligence and Neuroscience

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This paper presents an in-depth study and analysis of the model of English writing using artificial intelligence algorithms of neural networks. Based on word vectors, the unsupervised disambiguation, and clustering of multimedia contexts extracted from massive online videos, the disambiguation accuracy reaches over 0.7, and the resulting small-scale multimedia context set can cover up to 90% of vocabulary learning tasks; user experiments show that the multimedia context learning system based on this method can improve the effectiveness and experience of ESL vocabulary learning, as well as the long-term word sense memory of learners. The results are 30% better. Based on the dependency grammatical relations and semantic metrics of collocations on a large-scale professional corpus, we established a collocation intention description and retrieval method in line with users’ linguistic cognition and doubled the usage rate of collocation retrieval on the actual deployment system after half a year, becoming a user “sticky” ESL writing aid, and further defined style. Dictionaries only provide basic lexical definitions, and, even if supported by example sentences, they still cannot meet the needs of ESL authors in terms of expressive accuracy and richness. However, the current machine translation is based on the black box deep neural network construction, and its translation process is not understandable and interactive. Among the three algorithmic models constructed in this paper, the multitask learning model outperforms the conditional random field model and the LSTM-CRF model because the multitask learning model with auxiliary tasks solves the problem of sparse data to a certain extent, allowing the model to be trained more adequately in the case of uneven label distribution, and thus performs better than other models in the task of grammatical error detection.

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Section: Analytical Design Of English Writing Neural Network Algorithmmentioning

confidence: 99%

Construction of an Artificial Intelligence Writing Model for English Based on Fusion Neural Network Model

Hsiao

Hung

2022

Computational Intelligence and Neuroscience

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“…In the same manner, the emergence and rapid growth of research and development in the field of Internet of things have positively affected the research trends due to the integration of cloud computing, 5G and big data sentiment classification [67,68]. It would be appropriate to mention that the IoT industry is taking benefit of big data sentiment classification in reshaping, analysing and improving the sensorbased integrated information processing systems [68,69]. Sentiment analysis is computational processing of public views and opinions.…”

Section: Literature Reviewmentioning

confidence: 99%

IoT-Based Pervasive Sentiment Analysis: A Fine-Grained Text Normalization Framework for Context Aware Hybrid Applications

Habib¹,

Raza²

2021

Information and Knowledge in Internet of Things

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The proliferation of Web 2.0 has altered the general styles of research and academia. The Internet of things (IoT) is one of the core IT fields in the current arena, as it transforms the physical objects into sensor-based virtual intelligent objects. There exist millions of physical objects that are interlinked with sensor-based devices in order to mimic the real-world intelligence. The IoT industry is taking benefit of big data sentiment classification in reshaping and improving the sensor-based integrated information processing systems. Sentiment analysis (SA) proved to be the backbone in orientation and betterment of IoT technology. Opinion mining is the problem of natural language processing (NLP) and field of data mining and machine learning. The goal of opinion mining is the identification of public sentiments, feeling and appraisals. Opinion mining is the significant research area in this new arena of web mining and text classification. It draws great attention in the last few years due to social media sites and their usage. Proliferation of internet applications has changed the public communication trends. Online users adopt social media sites for communication and collaboration. Most valuable communication channels are Twitter, Facebook, Tumbler and Plurk. There exist various classes of webbased application for communication, i.e. discussion forum, blogs, review sites and

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“…Big Data Sentiment Analysis (BDSA) has developed into a very hot topic, attracting wide attention from all investors and stakeholders from the global academic community, industry, and government. All of these also apply to IoT datasets [9,[14][15][16][17]. The Internet of things (IoT) enables massive terminal connections and generates massive amounts of data through high-speed Internet and sensor technologies [18].…”

Section: Introductionmentioning

confidence: 99%

Dual‐Level Attention Based on a Heterogeneous Graph Convolution Network for Aspect‐Based Sentiment Classification

Yuan

Jiang

Liu

et al. 2021

Wireless Communications and Mobile Computing

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With the development of 5G, the advancement of basic infrastructure has led to considerable development in related research and technology. It also promotes the development of various smart devices and social platforms. More and more people are now using smart devices to post their reviews right after something happens. In order to keep pace with this trend, we propose a method to analyze users’ sentiment by using their text data. When analyzing users’ text data, it is noted that a user’s review may contain many aspects. Traditional text classification methods used by smart devices, however, usually ignore the importance of multiple aspects of a review. Additionally, most algorithms usually ignore the network structure information between the words in a sentence and the sentence itself. To address these issues, we propose a novel dual-level attention-based heterogeneous graph convolutional network for aspect-based sentiment classification which minds more context information through information propagation along with graphs. Particularly, we first propose a flexible HIN (heterogeneous information network) framework to model the user-generated reviews. This framework can integrate various types of additional information and capture their relationships to alleviate semantic sparsity of some labeled data. This framework can also leverage the full advantage of the hidden network structure information through information propagation along with graphs. Then, we propose a dual-level attention-based heterogeneous graph convolutional network (DAHGCN), which includes node-level and type-level attentions. The attention mechanisms can analyze the importance of different adjacent nodes and the importance of different types of nodes for the current node. The experimental results on three real-world datasets demonstrated the effectiveness and reliability of our model.

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Multimodality Sentiment Analysis in Social Internet of Things Based on Hierarchical Attentions and CSAT-TCN With MBM Network

Cited by 50 publications

References 25 publications

Construction of an Artificial Intelligence Writing Model for English Based on Fusion Neural Network Model

Construction of an Artificial Intelligence Writing Model for English Based on Fusion Neural Network Model

IoT-Based Pervasive Sentiment Analysis: A Fine-Grained Text Normalization Framework for Context Aware Hybrid Applications

Dual‐Level Attention Based on a Heterogeneous Graph Convolution Network for Aspect‐Based Sentiment Classification

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