Alapfogalmak
Combining lexicon-based methods with transformer models to enhance sentiment analysis in finance.
Kivonat
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
Statisztikák
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.
Idézetek
"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."