Granular computing-based multi-level interactive attention networks for targeted sentiment analysis

In recent years, targeted sentiment analysis has received great attention as a fine-grained sentiment analysis. Determining the sentiment polarity of a specific target in a sentence is the main task. This paper proposes a multi-channel convolutional neural network (MCL-CNN) for targeted sentiment classification. Our approach can not only parallelize over the words of a sentence, but also extract local features effectively. Contexts and targets can be more comprehensively utilized by using part-of-speech information, semantic information and interactive information, so that diverse features can be obtained. Finally, experimental results on the SemEval 2014 dataset demonstrate the effectiveness of this method.

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“…We use the matrix-based interactive attention proposed by [11], which can get great interactive information according to the formula as follows:…”

Section: Matrix-based Interactive Attention Mechanismmentioning

confidence: 99%

Multi-Channel Convolutional Neural Network for Targeted Sentiment Classification

Yuan

Zhao

et al. 2019

2019 International Conference on Machine Learning and Cybernetics (ICMLC)

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“…The existing knowledge representation systems cover many issues [8][9][10]; the most common ones are as follows: − Correspondence between database attributes and ontological attributes for establishing connection between the elements of a database and those of ontologies − Semantic similarity of acquired knowledge: The calculation of semantic similarity through identifying knowledge sources which are equal weight − Calculation of the distances between knowledge sources for a better extraction − Classification of collection measures − Identification of a new knowledge source.…”

Section: Introductionmentioning

confidence: 99%

Intelligent machine for ontological representation of massive pedagogical knowledge based on neural networks

Hadioui

Touimi

Faddouli

et al. 2021

IJECE

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Higher education is increasingly integrating free learning management systems (LMS). The main objective underlying such systems integration is the automatization of online educational processes for the benefit of all the involved actors who use these systems. The said processes are developed through the integration and implementation of learning scenarios similar to traditional learning systems. LMS produce big data traces emerging from actors’ interactions in online learning. However, we note the absence of instruments adequate for representing knowledge extracted from big traces. In this context, the research at hand is aimed at transforming the big data produced via interactions into big knowledge that can be used in MOOCs by actors falling within a given learning level within a given learning domain, be it formal or informal. In order to achieve such an objective, ontological approaches are taken, namely: mapping, learning and enrichment, in addition to artificial intelligence-based approaches which are relevant in our research context. In this paper, we propose three interconnected algorithms for a better ontological representation of learning actors’ knowledge, while premising heavily on artificial intelligence approaches throughout the stages of this work. For verifying the validity of our contribution, we will implement an experiment about knowledge sources example.

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Introduction

Zheng,

2024

Studies in Fuzziness and Soft Computing

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Granular computing-based multi-level interactive attention networks for targeted sentiment analysis

Cited by 7 publications

References 31 publications

Multi-Channel Convolutional Neural Network for Targeted Sentiment Classification

Multi-Channel Convolutional Neural Network for Targeted Sentiment Classification

Intelligent machine for ontological representation of massive pedagogical knowledge based on neural networks

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