The comparison and analysis of Skip-gram and CBOW in creating financial sentimental dictionary

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.