Big Data: Deep Learning for financial sentiment analysis

Sohangir, Sahar; Wang, Dingding; Pomeranets, Anna; Khoshgoftaar, Taghi M.

doi:10.1186/s40537-017-0111-6

Cited by 258 publications

(120 citation statements)

References 64 publications

(53 reference statements)

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“…Many studies use the same process for sentiment analysis. First, text features are automatically extracted from different data sources, then they are transferred into word embedding using the Word2vec tool [5,10,47].…”

Section: Related Workmentioning

confidence: 99%

Sentiment Analysis Based on Deep Learning: A Comparative Study

2020

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The study of public opinion can provide us with valuable information. The analysis of sentiment on social networks, such as Twitter or Facebook, has become a powerful means of learning about the users' opinions and has a wide range of applications. However, the efficiency and accuracy of sentiment analysis is being hindered by the challenges encountered in natural language processing (NLP). In recent years, it has been demonstrated that deep learning models are a promising solution to the challenges of NLP. This paper reviews the latest studies that have employed deep learning to solve sentiment analysis problems, such as sentiment polarity. Models using term frequency-inverse document frequency (TF-IDF) and word embedding have been applied to a series of datasets. Finally, a comparative study has been conducted on the experimental results obtained for the different models and input features.

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Section: Related Workmentioning

confidence: 99%

Sentiment Analysis Based on Deep Learning: A Comparative Study

2020

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“…Companies from all sectors and all over the world are using BD with DL extensively. This makes DL a valuable tool within BD ecosystem [65] since one of the its greatest assets is to analyze a massive sum of data. Several applications and solutions of BD with DL in academia and industry fall into areas such as computer vision, natural language processing, finance, remote sensing, transportation, marketing and advertising, and education.…”

Section: Applications In Technological Financial and Other Successfmentioning

confidence: 99%

Deep Learning and Big Data in Healthcare: A Double Review for Critical Beginners

et al. 2019

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In the last few years, there has been a growing expectation created about the analysis of large amounts of data often available in organizations, which has been both scrutinized by the academic world and successfully exploited by industry. Nowadays, two of the most common terms heard in scientific circles are Big Data and Deep Learning. In this double review, we aim to shed some light on the current state of these different, yet somehow related branches of Data Science, in order to understand the current state and future evolution within the healthcare area. We start by giving a simple description of the technical elements of Big Data technologies, as well as an overview of the elements of Deep Learning techniques, according to their usual description in scientific literature. Then, we pay attention to the application fields that can be said to have delivered relevant real-world success stories, with emphasis on examples from large technology companies and financial institutions, among others. The academic effort that has been put into bringing these technologies to the healthcare sector are then summarized and analyzed from a twofold view as follows: first, the landscape of application examples is globally scrutinized according to the varying nature of medical data, including the data forms in electronic health recordings, medical time signals, and medical images; second, a specific application field is given special attention, in particular the electrocardiographic signal analysis, where a number of works have been published in the last two years. A set of toy application examples are provided with the publicly-available MIMIC dataset, aiming to help the beginners start with some principled, basic, and structured material and available code. Critical discussion is provided for current and forthcoming challenges on the use of both sets of techniques in our future healthcare.

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“…As data are large and complex, it is difficult to extract the discriminative features of borrowers by employing existing data mining and machine learning techniques (Sohangir, Wang, Pomeranets, & Khoshgoftaar, ). Other methods that can extract meaningful patterns are required in big data.…”

Section: Related Workmentioning

confidence: 99%

Predicting repayment of borrows in peer‐to‐peer social lending with deep dense convolutional network

Kim

Cho

2019

Expert Systems

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In peer‐to‐peer lending, it is important to predict the repayment of the borrower to reduce the lender's financial loss. However, it is difficult to design a powerful feature extractor for predicting the repayment as user and transaction data continue to increase. Convolutional neural networks automatically extract useful features from big data, but they use only high‐level features; hence, it is difficult to capture a variety of representations. In this study, we propose a deep dense convolutional network for repayment prediction in social lending, which maintains the borrower's semantic information and obtains a good representation by automatically extracting important low‐ and high‐level features simultaneously. We predict the repayment of the borrower by learning discriminative features depending on the loan status. Experimental results on the Lending Club dataset show that our model is more effective than other methods. A fivefold cross‐validation is performed to run the experiments.

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Big Data: Deep Learning for financial sentiment analysis

Cited by 258 publications

References 64 publications

Sentiment Analysis Based on Deep Learning: A Comparative Study

Sentiment Analysis Based on Deep Learning: A Comparative Study

Deep Learning and Big Data in Healthcare: A Double Review for Critical Beginners

Predicting repayment of borrows in peer‐to‐peer social lending with deep dense convolutional network

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