Paper
28 March 2023 Emotion analysis method based on emotion intensity fusion and BiGRU
Haoyang Zhang, Changming Zhu
Author Affiliations +
Proceedings Volume 12566, Fifth International Conference on Computer Information Science and Artificial Intelligence (CISAI 2022); 1256602 (2023) https://doi.org/10.1117/12.2667864
Event: Fifth International Conference on Computer Information Science and Artificial Intelligence (CISAI 2022), 2022, Chongqing, China
Abstract
In Chinese sentiment analysis, sentiment words are just a drop in the ocean compared with the whole corpus. In order to solve the problem of insufficient emotion lexicon and prior knowledge, proposes a method to predict the emotion intensity of target words based on neural network model (Neural Network Emebdding Score, NNES). By training a small number of labeled samples, using clustering algorithm to find the seed words, calculate the similarity between the target words and the seed words, and using it as the input of neural network to predict the emotional intensity of the unlabeled words. Compared with the traditional machine learning regression models, it has smaller mean square error. Meanwhile, a BiGRU model based on attention mechanism and convolution is proposed by integrating the predicted emotion intensity with word vector (Neural Network Emebdding Score with CNN and Attention-BiGRU, NNESC-Att-BiGRU). To compare several popular models on product and hotel review data sets, and the proposed model has better classification effect on Chinese sentiment classification task.
© (2023) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Haoyang Zhang and Changming Zhu "Emotion analysis method based on emotion intensity fusion and BiGRU", Proc. SPIE 12566, Fifth International Conference on Computer Information Science and Artificial Intelligence (CISAI 2022), 1256602 (28 March 2023); https://doi.org/10.1117/12.2667864
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KEYWORDS
Neural networks

Convolution

Education and training

Machine learning

Associative arrays

Error analysis

Performance modeling

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