Dependency Parsing of Financial News to Improve Sentiment Analysis for Predicting Market Prices

Wang, Long; Wang, Hung-Hsuan; Wang, Long

doi:10.1109/taai51410.2020.00009

Cited by 5 publications

(2 citation statements)

References 14 publications

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“…Li and Li (2022) applied syntactic analysis to effectively preserve the semantic rules and structural dependencies of short texts. Wang et al (2020) identified the polarity of sentence constituents through dependency syntax and then combined it with a sentiment lexicon for sentiment analysis. Hou et al (2021) inventively integrated dependency parse trees into a GCN model for sentiment classification.…”

Section: Dependency Parsingmentioning

confidence: 99%

Linking negative herding effect to online public opinion: A new BiGADG approach for sentiment classification

Zhou,

Hu,

Liu

et al. 2024

Expert Systems

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The negative herding effect tends to cause the spread of negative emotions among netizens, resulting in significant public opinion crises. Therefore, linking the negative herding effect and online public opinion, we aim to exploring the connection between netizens' emotions, opinions, and the negative herding effect. To better distinguish the emotions in texts, a new BiGADG (Bidirectional Gated recurrent unit based on Attention with Distance‐based dependency syntax Graph convolutional network) model is proposed in this article. This model includes primarily the proposed dependency syntax based on dependency distance, Bidirectional Gated Recurrent Unit (BiGRU), multi‐head self‐attention and Graph Convolutional Network (GCN). Particularly, the dependency syntax based on dependency distances incorporates word‐to‐word dependency distances into the traditional dependency syntax, which enriches the syntactic structural features of the text. After the experiments, the accuracy of BiGADG model is 86.53%, which is 3.11% higher compared to BERT model. It is evident that our model for sentiment classification has been improved. Based on the results of sentiment classification, we investigate the existence of negative herding behaviour and its underlying motivation in online public opinion. We have found that the negative herding effect, if not effectively managed, may trigger large‐scale and uncontrollable public opinion crises.

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Section: Dependency Parsingmentioning

confidence: 99%

Linking negative herding effect to online public opinion: A new BiGADG approach for sentiment classification

Zhou,

Hu,

Liu

et al. 2024

Expert Systems

View full text Add to dashboard Cite

show abstract

“…Recently, in an environment where opinions among users through huge amounts of web pages are actively exchanged, an efficient analysis and mining method of website data is needed for accurate sentiment analysis. [18] proposed an advanced sentiment classification method in financial news articles and proposed a market price prediction method. In this work, customized crawlers extract financial news articles for the target region(industry) from multiple sources.…”

Section: Related Workmentioning

confidence: 99%

Efficient Sentiment-Aware Web Crawling Methods for Constructing Sentiment Dictionary

Shin

et al. 2021

IEEE Access

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Cultural adaptation and sentiment analysis based on neuron coding in the context of new media communication

Zhu

2023

Soft Comput

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Dependency Parsing of Financial News to Improve Sentiment Analysis for Predicting Market Prices

Cited by 5 publications

References 14 publications

Linking negative herding effect to online public opinion: A new BiGADG approach for sentiment classification

Linking negative herding effect to online public opinion: A new BiGADG approach for sentiment classification

Efficient Sentiment-Aware Web Crawling Methods for Constructing Sentiment Dictionary

Cultural adaptation and sentiment analysis based on neuron coding in the context of new media communication

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