From Statistical to Deep Learning Models: A Comparative Sentiment Analysis Over Commodity News

Abstract

The sentiment analysis of news and social media posts is a growing research area with advancements in natural language processing and deep learning techniques. Although various studies addressing the extraction of the sentiment score from news and other resources for specified stocks or a stock index, still there is a lack of an analysis of the sentiment in more specialized topics such as commodity news. In this paper, several natural language processing techniques with a varying range from statistical methods to deep learning-based methods were applied on the commodity news. Firstly, the dictionary-based methods were investigated with the most common dictionaries in financial sentiment analysis such as Loughran & McDonald and Harvard dictionaries. Then, statistical models have been applied to the commodity news with count vectorizer and TF-IDF. The compression-based NCD has been also included to test on the labeled data. To improve the results of the sentiment extraction, the news data was processed by deep learning-based state-of-art models such as ULMFit, Flair, Word2Vec, XLNet, and BERT. A comprehensive analysis of all tested models was held. The final analysis indicated the performance difference between the deep learningbased and statistical models for the sentiment analysis task on the commodity news. BERT has achieved superior performance among the deep learning models for the given data.

Artificial Intelligence in stock broking: A systematic review of strategies and outcomes

“…Furthermore, natural language processing techniques have been instrumental in analyzing the effect of news and public mood on stock movements (Li et al, 2014;Adeniyi et al, 2020). Studies have demonstrated the use of NLP in sentiment analysis and event extraction for stock forecasting, highlighting the importance of sentiment, semantic, and eventextraction-based approaches in stock forecasting (Sivri et al, 2021;Cheng et al, 2022). Moreover, the historical evolution of NLP has paved the way for its application in stock broking, enabling the extraction of valuable insights from textual data related to stock market trends (Nadkarni et al, 2011;Oti and Ayeni, 2013).…”