Big Data: Deep Learning for financial sentiment analysis

By Sahar Sohangir, Dingding Wang, Anna Pomeranets, Taghi M. Khoshgoftaar. 2018

347 citations recorded by the source corpus.

Deep Learning and Big Data analytics are two focal points of data science. Deep Learning models have achieved remarkable results in speech recognition and computer vision in recent years. Big Data is important for organizations that need to collect a huge amount of data like a social network and one of the greatest assets to use Deep Learning is analyzing a massive amount of data (Big Data). This advantage makes Deep Learning as a valuable tool for Big Data. Deep Learning can be used to extract incredible information that buried in a Big Data. The modern stock market is an example of these social networks. They are a popular place to increase wealth and generate income, but the fundamental problem of when to buy or sell shares, or which stocks to buy has not been solved. It is very common among investors to have professional financial advisors, but what is the best resource to support the decisions these people make? Investment banks such as Goldman Sachs, Lehman Brothers, and Salomon Brothers dominated the world of financial advice for more than a decade. However, via the popularity of the Internet and financial social networks such as StockTwits and SeekingAlpha, investors around the world have new opportunity to gather and share their experiences. Individual experts can predict the movement of the stock market in financial social networks with the reasonable accuracy, but what is the sentiment of a mass group of these expert authors towards various stocks? In this paper, we seek to determine if Deep Learning models can be adapted to improve the performance of sentiment analysis for StockTwits. We applied several neural network models such as long short-term memory, doc2vec, and convolutional neural networks, to stock market opinions posted in StockTwits. Our results show that Deep Learning model can be used effectively for financial sentiment analysis and a convolutional neural network is the best model to predict sentiment of authors in StockTwits dataset.

Subfields: Artificial Intelligence, Electrical and Electronic Engineering, Management Science and Operations Research

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Citations in the corpus

Deep Learning for Financial Applications : A Survey (2020, methodology): “[189] News from NowNews, AppleDaily, LTN, MoneyDJ”
FinBERT: Financial Sentiment Analysis with Pre-trained Language Models (2019, methodology): “Sohangir et al. (2018) [26] apply several generic neural network architectures to a StockTwits dataset, finding CNN as the best performing neural network architecture.”
Sentiment Analysis Based on Deep Learning: A Comparative Study (2020, methodology): “First, text features are automatically extracted from different data sources, then they are transferred into word embedding using the Word2vec tool [5,10,47].”
Improving the Interpretability of Neural Sentiment Classifiers via Data Augmentation (2019, methodology): “Sentiment analysis is one of the most widely-used applications of natural language processing (NLP) in the financial services industry (Sohangir et al., 2018; Daniel et al., 2017; Li et al., 2018; Chen et al., 2016), where neural sentiment classifiers help enterprises gauge public opinion, conduct m”