Ranking based multi-label classification for sentiment analysis

Autor:	Jianfei Zhang, Zhang Xiong, Wenge Rong, Dengbo Chen
Rok vydání:	2020
Předmět:	Statistics and Probability Multi-label classification Artificial Intelligence Computer science business.industry Sentiment analysis General Engineering Artificial intelligence business computer.software_genre computer Natural language processing Ranking (information retrieval)
Zdroj:	Journal of Intelligent & Fuzzy Systems. 39:2177-2188
ISSN:	1875-8967 1064-1246
DOI:	10.3233/jifs-179882
Popis:	This paper proposes a sentiment analysis framework based on ranking learning. The framework utilizes BERT model pre-trained on large-scale corpora to extract text features and has two sub-networks for different sentiment analysis tasks. The first sub-network of the framework consists of multiple fully connected layers and intermediate rectified linear units. The main purpose of this sub-network is to learn the presence or absence of various emotions using the extracted text information, and the supervision signal comes from the cross entropy loss function. The other sub-network is a ListNet. Its main purpose is to learn a distribution that approximates the real distribution of different emotions using the correlation between them. Afterwards the predicted distribution can be used to sort the importance of emotions. The two sub-networks of the framework are trained together and can contribute to each other to avoid the deviation from a single network. The framework proposed in this paper has been tested on multiple datasets and the results have shown the proposed framework’s potential.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::797055e2e5ab5eaff8a02d226c37bf3b https://doi.org/10.3233/jifs-179882 Zobrazit plný text záznamu Plný text ve formátu PDF Plný text ve formátu HTML
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