Identification of fact-implied implicit sentiment based on multi-level semantic fused representation

Jian, Lin; Wang, Suge; Li, Deyu

doi:10.1016/j.knosys.2018.11.023

Cited by 49 publications

(33 citation statements)

References 11 publications

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“…Then, under the guidance of multigranulation probabilistic models, we utilize the model of INSs, MGRSs and PRSs to handle the above-mentioned challenges. Moreover, compared with existing popular nonlinear modeling approaches, such as formal concept analysis [33,34,58,[64][65][66][67], control systems [59,60] and sentiment analysis [61][62][63]68,69], which neither effectively handle indeterminate and incomplete information in complicated MAGDM problems, nor reasonably fuse and analyze multi-source information with incorrect and noisy data, it is necessary to combine INSs, MGRSs with PRSs to develop some meaningful hybrid models along with corresponding MAGDM approaches. In light of MAGDM procedures in the current section, we sum up the merits of the proposed MAGDM algorithm below:…”

Section: Discussionmentioning

confidence: 99%

“…Finally, a real-world example is employed to prove the validity of the established decision-making rule. In addition, it is noteworthy that plenty of interesting nonlinear modeling approaches have been proved to be successful in various applications [58][59][60][61][62][63][64][65][66][67]. For instance, Medina and Ojeda-Aciego [58] applied multi-adjoint frameworks to general t-concept lattice, and some other works on fuzzy formal contexts based on GrC-based approaches were explored in succession [64][65][66][67].…”

Section: The Contributions Of the Researchmentioning

confidence: 99%

“…Gil et al [60] studied a surrogate model based optimization of traffic lights cycles and green period ratios by means of microscopic simulation and fuzzy rule interpolation. Smarandache et al [61] explored word-level sentiment similarities in the context of NSs, and some other meaningful works on word-level sentiment analysis were also investigated recently [62,63,68,69].…”

Section: The Contributions Of the Researchmentioning

confidence: 99%

See 2 more Smart Citations

Multi-Attribute Group Decision Making Based on Multigranulation Probabilistic Models with Interval-Valued Neutrosophic Information

Zhang

Kang

et al. 2020

Mathematics

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In plenty of realistic situations, multi-attribute group decision-making (MAGDM) is ubiquitous and significant in daily activities of individuals and organizations. Among diverse tools for coping with MAGDM, granular computing-based approaches constitute a series of viable and efficient theories by means of multi-view problem solving strategies. In this paper, in order to handle MAGDM issues with interval-valued neutrosophic (IN) information, we adopt one of the granular computing (GrC)-based approaches, known as multigranulation probabilistic models, to address IN MAGDM problems. More specifically, after revisiting the related fundamental knowledge, three types of IN multigranulation probabilistic models are designed at first. Then, some key properties of the developed theoretical models are explored. Afterwards, a MAGDM algorithm for merger and acquisition target selections (M&A TSs) with IN information is summed up. Finally, a real-life case study together with several detailed discussions is investigated to present the validity of the developed models.

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Section: Discussionmentioning

confidence: 99%

Section: The Contributions Of the Researchmentioning

confidence: 99%

Section: The Contributions Of the Researchmentioning

confidence: 99%

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Multi-Attribute Group Decision Making Based on Multigranulation Probabilistic Models with Interval-Valued Neutrosophic Information

Zhang

Kang

et al. 2020

Mathematics

Self Cite

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“…However, word order features were not considered in TB-CNN. Subsequently, Liao et al [23] proposed a multilayer convolutional neural network model, the semantic dependency tree-based CNN (SDT-CNN), based on the syntactic dependency tree, which can learn implicit dependency representation and a context-explicit semantic background representation of emotional information in the text. However, the context of each sentence in the SDT-CNN model is irrelevant, which may cause some semantic deficiencies.…”

Section: Related Work a Sentiment Analysismentioning

confidence: 99%

Context-Specific Heterogeneous Graph Convolutional Network for Implicit Sentiment Analysis

et al. 2020

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Sentiment analysis has attracted considerable attention in recent years. In particular, implicit sentiment analysis is a more challenging problem due to the lack of sentiment words. It requires us to combine contextual information and precisely understand the emotion changing process. Graph convolutional network (GCN) techniques have been widely applied for sentiment analysis since they are capable of learning from complex structures and preserving global information. However, these models either only focus on extracting features from a single sentence and ignore the context semantic background or only consider the textual information and overlook the phrase dependency when constructing the graph. To address these problems, we propose a new context-specific heterogeneous graph convolutional network (CsHGCN) framework that can combine all context representations. It has a complete context that reflects the information on documents more comprehensively. It has a dependency structure that obtains token-token semantic acquisition more accurately. The experimental results on a Chinese implicit sentiment dataset show that our proposed model can effectively identify the target sentiment of sentences, and visualization of the attention layers further demonstrates that the model selects qualitatively informative tokens and sentences.

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“…On Restaurant dataset, The probability of co-occurrence aspects is 50.11%, this figure is 37.48% on Laptop dataset and only 0.001% on the Twitter dataset. Second, the Laptop dataset contains more implicit sentiments [52], such as ''lots of preloaded software''. Similarly, Twitter as a social community, the dataset built on it contains more sophisticated speech act [53].…”

Section: ) the Ability To Handle Aspects Co-occurrencementioning

confidence: 99%

Aspect-Context Interactive Attention Representation for Aspect-Level Sentiment Classification

Jian

et al. 2020

IEEE Access

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Aspect-level sentiment classification aims to determine sentiment polarities of various aspects in reviews, where each review typically contains multiple aspects, that may correspond to different polarities. Aspect-level sentiment classification, unlike document-level sentiment classification, requires different context representations for different aspects. Existing methods normally use Long Short-Term Memory (LSTM) network to model aspects and contexts separately, and they combine attention mechanisms to extract features of a specific aspect in its context. Attention mechanisms are not used for sequence modeling, so aspects are not considered when generating context sequence representations. This study proposes a novel aspect-context interactive representation structure that only relies on an attention mechanism for generating sequence-to-sequence representations in both context and aspect. It is capable of extracting features related to the specific aspect during the process of its context sequence modeling, and generating a high quality aspect representation simultaneously. We have conducted comprehensive experiments to compare with thirteen existing methods. Our experimental results show that the proposed model is able to achieve significantly better performance on Restaurant dataset, as well as very competitive results on Laptop and Twitter datasets. INDEX TERMS Aspect-level sentiment classification, attention mechanism, interactive representation, natural language processing.

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Identification of fact-implied implicit sentiment based on multi-level semantic fused representation

Cited by 49 publications

References 11 publications

Multi-Attribute Group Decision Making Based on Multigranulation Probabilistic Models with Interval-Valued Neutrosophic Information

Multi-Attribute Group Decision Making Based on Multigranulation Probabilistic Models with Interval-Valued Neutrosophic Information

Context-Specific Heterogeneous Graph Convolutional Network for Implicit Sentiment Analysis

Aspect-Context Interactive Attention Representation for Aspect-Level Sentiment Classification

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