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FCM vectorization for Twitter sentimental analysis using multi stacked BiLSTM. [PDF]

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Gomathi R, Saranya K, Munirathinam T, Karthiga M.   +3 more
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Detecting Dependency-Related Sentiment Features for Aspect-Level Sentiment Classification

IEEE Transactions on Affective Computing, 2023
Aspect level sentiment classification aims to classify the sentiment polarity of a given aspect term or aspect category in a sentence. For sentiment classification towards a given aspect term, since a sentence may contain more than one aspect term, there may exist some opinions which are not the modifiers of the given aspect term.
Xing Zhang, Jingyun Xu, Yi Cai 0001, Xingwei Tan, Changxi Zhu   +4 more
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Relation construction for aspect-level sentiment classification

Information Sciences, 2022
Abstract Aspect-level sentiment classification aims to obtain fine-grained sentiment polarities of different aspects in one sentence. Most existing approaches handle the classification by acquiring the importance of context words towards each given aspect individually, and ignore the benefits brought by aspect relations.
Jiandian Zeng, Tianyi Liu, Weijia Jia 0001, Jiantao Zhou 0001   +3 more
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Aspect sentiment learning for Aspect-Level Sentiment Classification

Neural Networks, 2023
Aspect-Level Sentiment Classification (ALSC) is a fine-grained Sentiment Analysis (SA) task that aims to determine the sentiments of a sentence toward different aspects. Despite their significant success, most existing methods derive aspect sentiment semantics from individual sentences, overlooking the interrelationships among relevant sentences that ...
Zhongquan Jian, Jiajian Li, Meihong Wang, Junfeng Yao, Qingqiang Wu   +4 more
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Ensemble correction model for aspect-level sentiment classification

Journal of Information Science, 2022
The aspect-level sentiment analysis is widely used in public opinion analysis. However, the problem of context information loss and distortion with the increase of the model depth is rarely considered in previous research. Few studies have attempted to combine the feature extracted from different embedding models.
Yiwen Zhou, Lu An, Gang Li, Chuanming Yu
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Aspect-level Sentiment Classification with Reinforcement Learning

2019 International Joint Conference on Neural Networks (IJCNN), 2019
Aspect-level sentiment classification aims to predict the sentiment polarity of a given aspect in a sentence. However, most of the existing methods focus on the information of the entire sentence rather than a segment that describes the aspect, making it difficult to identify the mapping between an aspect and a segment.
Tingting Wang, Jie Zhou 0015, Qinmin Vivian Hu, Liang He 0001   +3 more
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A Convolutional Neural Network for Aspect-Level Sentiment Classification

International Journal of Pattern Recognition and Artificial Intelligence, 2019
Sentiment analysis, including aspect-level sentiment classification, is an important basic natural language processing (NLP) task. Aspect-level sentiment can provide complete and in-depth results. Words with different contexts variably influence the aspect-level sentiment polarity of sentences, and polarity varies based on different aspects of a ...
Yongping Xing, Chuangbai Xiao, Yifei Wu, Ziming Ding   +3 more
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Effective Attention Networks for Aspect-level Sentiment Classification

2018 10th International Conference on Knowledge and Systems Engineering (KSE), 2018
This paper deals with the aspect-level sentiment classification which identifies the sentiment polarity of a specific aspect of its context. We introduce novel attention networks by using the benefits of Long Short-Term Memory (LSTM), Attention mechanisms and Lexicons to form an aspect-specific representation.
Huy-Thanh Nguyen, Minh-Le Nguyen 0001
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Syntactic and semantic analysis network for aspect-level sentiment classification

Applied Intelligence, 2021
Aspect-level sentiment classification aims to predict sentiment polarities for different aspect terms within the same sentence or document. However, existing methods rely heavily on modeling the semantic relevance of an aspect term and its context words, and ignore the importance of syntax analysis to a certain extent.
Dianyuan Zhang, Zhenfang Zhu, Shiyong Kang, Guangyuan Zhang, Peiyu Liu 0001   +4 more
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