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The comparison and analysis of Skip-gram and CBOW in creating financial sentimental dictionary
- 1 Faculty of Science and Technology, University of Macao, Macao, China
- 2 Smart Education, Jiangsu Normal University, Xuzhou, Jiangsu, China
* Author to whom correspondence should be addressed.
Abstract
Textual analysis is increasingly used in various fields due to data availability, computing power, and machine learning techniques. In finance, sentiment analysis is essential for obtaining excess returns, and building domain-specific lexicons using word2vec is a prevalent method. The CBOW and Skip-gram algorithms have different predictive methodologies and performances depending on the task and dataset. This paper reviews financial sentiment analysis using a dictionary method and compares the performance of the two algorithms. CBOW trains faster than Skip-gram when dealing with a small amount of text data, but as the amount of data increases, Skip-gram becomes more efficient. Besides, the Skip-gram captures more synonyms of the selected words than CBOW.
Keywords
word2vec, CBOW, skip-gram, financial sentiment analysis
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Cite this article
Zhang,X.;Zhang,L. (2024). The comparison and analysis of Skip-gram and CBOW in creating financial sentimental dictionary. Applied and Computational Engineering,44,56-67.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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