WITHDRAWN: Financial Investor Sentiment Analysis Based on FPGA and Convolutional Neural Network

Dong, Jiahui

doi:10.1016/j.micpro.2020.103418

Cited by 7 publications

(3 citation statements)

References 15 publications

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“…In the past, many kinds of recurrent neural network architectures have been widely used in normal language handling and accomplished great outcomes. With the continuous advancement of profound learning hypothesis and innovation, individuals step by step find that convolutional neural organization additionally has a solid capacity in the field of text handling [14,15]. Therefore, in the analysis of financial texts, we will also try to build a model based on CNN.…”

Section: Convolutional Neural Networkmentioning

confidence: 99%

A Hybrid Intelligent System for Company Financial Risk Detection Based on Tree-Based Model and Deep Neural Network

Wei

2022

Wireless Communications and Mobile Computing

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At present, the process of security issuance in China has changed from examination and approval system to approval system. With the increasing stock that enters the capital market through the IPO, establishing an effective financial risk monitoring and control system of listed companies has great practical significance and application value. In the perspective of big data, this paper constructs a new practical financial early warning integration model. A total of 32,283 financial statements of 3,025 listed companies are collected from 2000 to 2017. First, this paper collected and built a financial crisis prediction database: a total of 32,283 financial statements of 3,025 listed companies occurred from year 2000 to 2017 and corresponding financial indicators including 6 primary indicators and 25 secondary indicators are collected. Next, the financial crisis model based on financial indicators is proposed and trained by the classic machine learning method such as random forest and gradient boosting decision tree. And then, through natural language processing and deep learning technology, this paper also builds a financial crisis prediction model based on financial statement text. Finally, an ensemble model is proposed and its performances are evaluated; the results indicate that the proposed ensemble model performed the best and significantly outperforms other benchmark methods for financial crisis prediction and identification, indicating that it can be employed as an intelligent identification system to enhance identification accuracy for early warning and identification of financial crisis of listed companies in China stock marketing.

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Section: Convolutional Neural Networkmentioning

confidence: 99%

A Hybrid Intelligent System for Company Financial Risk Detection Based on Tree-Based Model and Deep Neural Network

Wei

2022

Wireless Communications and Mobile Computing

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“…Nevertheless, the sentiment of text in continuous values could not be detected accurately with the NB. (J. Dong, 2020, [16]) recommended a financial investor sentiment analysis grounded on Field Programmable Gate Array. The fusion of a multi-window Convolutional Neural Network (CNN)-LSTM and a multi-window CNN was utilized.…”

Section: A Problem Statementmentioning

confidence: 99%

An Aggregator Framework for Transforming Big Data in Real-Time using PT-INDRNN

Sowmya¹,

R²

2023

IJEAT

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The prediction of stock market prices based on the financial text sentiment classification using Machine Learning (ML) and Deep Learning (DL) models is becoming popular among researchers in the era of Big Data (BD). Nevertheless, owing to the lack of extensive analysis, most of the developed ML and DL models failed to achieve better classification results. Thus, for the real-time prediction of the polarity of the stock price, a Probability Tanh-Independently Recurrent Neural Network (PT-IndRNN)-based classification of the sentiment of the financial text data of Twitter is proposed to solve this problem. Primarily, by employing the corresponding API, the real-time financial data and Twitter data are extracted and stored in the MongoDB database using Apache Flume. This stored data with the historical big datasets are taken and pre-processed. Next, by deploying the proposed Hadoop Distributed File System (HDFS) clustering, the pre-processed stock market data and Twitter data in real-time, as well as the historical dataset, are combined separately. After that, the features are extracted from the clustered sentences. Then, by utilizing the Senti Word Net, the sentences chosen using Linear Scaling-Dwarf Mongoose Optimization Algorithm (LS-DMOA) are converted to negative and positive scores. In the end, the sentiment of the financial texts is classified by the PTh-Ind RNN, which is proved by obtaining reliable result values.

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“…To alleviate the discussed shortcomings, a hybrid approach (i.e., a combination of machine learning methods and lexicons) can help improve the sentiment classification performance. Since the text classification problem is a supervised learning task in which the class observations is predicted based on some feature values, a wide range of ML algorithms (e.g., Support Vector Machine (SVM) [12,1], Naive Bayes (NB) [12,38], decision tree [12], random forest [12,1], logistic regression, and neural networks [27,47,9,7]) can be incorporated.…”

Section: Supervised Text Classificationmentioning

confidence: 99%

Leveraging Artificial Intelligence to Analyze Citizens' Opinions on Urban Green Space

Ghahramani¹,

Galle²,

Ratti³

et al. 2021

Preprint

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Continued population growth and urbanization is shifting research to consider the quality of urban green space over the quantity of these parks, woods, and wetlands. The quality of urban green space has been hitherto measured by expert assessments, including in-situ observations, surveys, and remote sensing analyses. Location data platforms, such as TripAdvisor, can provide people's opinion on many destinations and experiences, including UGS. This paper leverages Artificial Intelligence techniques for opinion mining and text classification using such platform's reviews as a novel approach to urban green space quality assessments. Natural Language Processing is used to analyze contextual information given supervised scores of words by implementing computational analysis. Such an application can support local authorities and stakeholders in their understanding of-and justification for-future investments in urban green space.

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WITHDRAWN: Financial Investor Sentiment Analysis Based on FPGA and Convolutional Neural Network

Cited by 7 publications

References 15 publications

A Hybrid Intelligent System for Company Financial Risk Detection Based on Tree-Based Model and Deep Neural Network

A Hybrid Intelligent System for Company Financial Risk Detection Based on Tree-Based Model and Deep Neural Network

An Aggregator Framework for Transforming Big Data in Real-Time using PT-INDRNN

Leveraging Artificial Intelligence to Analyze Citizens' Opinions on Urban Green Space

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