Abstract
With the rapid development of contemporary e-commerce and social media, the need of better understanding and exploring users’ evaluations on e-commercial products is becoming urgent and crucial, which results in the emergence of a new research hot-spot aiming at analysing and mining latent features of customer reviews on e-commercial products. In order to analyse the associations among semantic features with different lengths in e-commercial reviews, a text sentiment analysis method, named as ALBERT-SACR, is proposed based on self-adaptive context reasoning mechanism in this paper. Firstly, the global contextual features are extracted using the Transformer blocks of ALBERT. Then, semantic features with different lengths are extracted on the basis of multi-channel CNN combined with self-attention mechanism to perform context reasoning and adaptive adjustment of relational weights. Finally, a fully connected neural network is used for sentiment classification. public Chinese datasets Waimai and Shopping, where the performance of our method is qualitatively compared with five other methods. Simulation results verify both the effectiveness and efficiency of our proposed ALBERT-SACR, and the adaptive nature of our proposed SACR is effective in contextual inference for semantic features of different lengths.
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Acknowledgement
This work was supported by CCF Opening Project of Information System (CCFIS2021-03-01) and the Natural Science Project of Education Department of Shaanxi Province (No.21JK0646).
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Hou, S., Zhao, X., Liu, N., Shi, X., Wang, Y., Zhang, G. (2022). Self-adaptive Context Reasoning Mechanism for Text Sentiment Analysis. In: Zhao, X., Yang, S., Wang, X., Li, J. (eds) Web Information Systems and Applications. WISA 2022. Lecture Notes in Computer Science, vol 13579. Springer, Cham. https://doi.org/10.1007/978-3-031-20309-1_17
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