Sentiment Analysis in Finance: eXplainable Lexicons (XLex)

核心概念

Combining lexicon-based methods with transformer models to enhance sentiment analysis in finance.

要約

The article introduces eXplainable Lexicons (XLex) that combine lexicon-based methods and transformer models for sentiment analysis in finance. It demonstrates the advantages of XLex over traditional lexicons and transformer models, showcasing improved accuracy and efficiency in sentiment analysis of financial texts.

• Lexicon-based sentiment analysis in finance
• Challenges and opportunities in the financial industry
• Applications of sentiment analysis in finance
• Comparison of lexicon-based and machine learning approaches
• Introduction of XLex methodology for sentiment analysis
• Methodology for creating XLex and its advantages
• Evaluation of XLex performance in sentiment analysis
• Conclusion and future research directions

統計

Lexicon-based methods are simple and fast but require manual annotation efforts.
Transformer models are computationally expensive but offer superior performance.
XLex outperforms LM lexicon in sentiment analysis of financial datasets.
XLex+LM achieves higher accuracy improvement.
Lexicon-based approach is more efficient in terms of model speed and size compared to transformers.

引用

"Lexicon-based sentiment analysis in finance leverages specialized, manually annotated lexicons created by human experts to extract sentiment from financial texts effectively."
"XLex outperforms LM when applied to general financial texts, resulting in enhanced word coverage and an overall increase in classification accuracy."

抽出されたキーインサイト

Sentiment Analysis in Finance

by Maryan Rizin... 場所 arxiv.org 03-28-2024

https://arxiv.org/pdf/2306.03997.pdf

深掘り質問

How can XLex methodology be applied in other domains beyond finance?

The XLex methodology can be applied in other domains beyond finance by adapting the process of creating explainable lexicons to suit the specific vocabulary and context of those domains. The methodology involves leveraging transformer models and SHAP explainability to automatically learn and expand lexicons. By fine-tuning the process to cater to the language and terminology of different domains, XLex can be used to extract sentiment from texts in areas such as healthcare, marketing, social media analysis, and more. The key lies in customizing the lexicon creation process to capture the nuances and specific language patterns of each domain.

What are the limitations of using lexicon-based methods in sentiment analysis?

While lexicon-based methods in sentiment analysis have their advantages, they also come with limitations. One major limitation is the manual effort required to create, maintain, and update lexicons. This process can be time-consuming and may not capture all relevant words or sentiments accurately. Lexicons are also domain-specific, meaning they may not be easily transferable to other domains without significant modifications. Additionally, lexicon-based methods may struggle to capture the complexities of language, such as sarcasm, irony, or context-dependent sentiment, leading to potential inaccuracies in sentiment classification.

How can the interpretability of XLex models benefit financial decision-making beyond sentiment analysis?

The interpretability of XLex models can benefit financial decision-making by providing insights and explanations into the results of sentiment analysis. By using SHAP to explain the contributions of individual words to the sentiment classification, financial analysts and decision-makers can better understand the rationale behind the sentiment predictions. This interpretability can lead to more informed decision-making, especially in scenarios where understanding the sentiment of financial texts is crucial for making investment decisions, risk assessments, or market trend predictions. The transparency provided by XLex models can enhance the trust and confidence in the sentiment analysis results, ultimately improving the quality of financial decisions.