基于BERT 的金融文本情感分析模型

doi:10.12066/j.issn.1007-2861.2308

Abstract

Abstract:

n the financial sector, more and more investors choose to express their opinions on the internet platform. These comment texts can fully reflect investor sentiment and influence their investment decisions and market trends. Emotion analysis as an important branch of natural language processing (NLP), which provides an effective research means for analyzing a large number of text emotional types in financial sector. However, due to the professional nature of domain-specific texts and the inapplicability of large label data sets, text emotion analysis in the financial field has brought great challenges to the traditional emotion analysis model. When the general emotion analysis model is applied to specific fields such as finance, its accuracy and recall rate are poor. In order to overcome these challenges, a BERT (bidirectional encoder representations from Transformers) preprocessing model based on full word coverage and feature enhancement in financial field was proposed for the emotional analysis task of financial text from the perspective of word representation model.

Key words: sentiment analysis, word embedded vector, BERT, bag-of-POS (part of speech), named entity recognition

CLC Number:

TP 391.1

ZHU He, LU Xiaofeng, XUE Lei. Emotional analysis model of financial text based on the BERT[J]. Journal of Shanghai University（Natural Science Edition）, 2023, 29(1): 118-128.

Figures/Tables 7

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References 17

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Emotional analysis model of financial text based on the BERT

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