A Two-Dimensional Sentiment Analysis of Online Public Opinion and Future Financial Performance of Publicly Listed Companies

Yen, Meng-Feng; Huang, Yu-Pei; Yu, Liang-Chih; Chen, Yueh‐Ling ‐L

doi:10.1007/s10614-021-10111-y

Cited by 13 publications

(6 citation statements)

References 39 publications

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“…Quamer et al [ 22 ] proposed a self-attentive convolutional neural network model that can effectively perform sentence matching and natural language inference. Yen et al [ 36 ] used the text in online news and stock forums to conduct text exploration and predict future financial performance. Choi et al [ 3 ] proposed using text mining to analyze social network texts to identify cyber bullying.…”

Section: Literature Reviewmentioning

confidence: 99%

Analysis of agricultural exports based on deep learning and text mining

Hsu

2022

J Supercomput

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Agricultural exports are an important source of economic profit for many countries. Accurate predictions of a country’s agricultural exports month on month are key to understanding a country’s domestic use and export figures and facilitate advance planning of export, import, and domestic use figures and the resulting necessary adjustments of production and marketing. This study proposes a novel method for predicting the rise and fall of agricultural exports, called agricultural exports time series-long short-term memory (AETS-LSTM). The method applies Jieba word segmentation and Word2Vec to train word vectors and uses TF-IDF and word cloud to learn news-related keywords and finally obtain keyword vectors. This research explores whether the purchasing managers’ index (PMI) of each industry can effectively use the AETS-LSTM model to predict the rise and fall of agricultural exports. Research results show that the inclusion of keyword vectors in the PMI values of the finance and insurance industries has a relative impact on the prediction of the rise and fall of agricultural exports, which can improve the prediction accuracy for the rise and fall of agricultural exports by 82.61%. The proposed method achieves improved prediction ability for the chemical/biological/medical, transportation equipment, wholesale, finance and insurance, food and textiles, basic materials, education/professional, science/technical, information/communications/broadcasting, transportation and storage, retail, and electrical and machinery equipment categories, while its performance for the electrical and optical categories shows improved prediction by combining keyword vectors, and its accuracy for the accommodation and food service, and construction and real estate industries remained unchanged. Therefore, the proposed method offers improved prediction capacity for agricultural exports month on month, allowing agribusiness operators and policy makers to evaluate and adjust domestic and foreign production and sales.

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Section: Literature Reviewmentioning

confidence: 99%

Analysis of agricultural exports based on deep learning and text mining

Hsu

2022

J Supercomput

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“…The empirical results illustrated the proposed emotional scores and oil prices can improve the prediction accuracy for agricultural product prices effectively. Yen et al ( 2021 ) presented a novel sentiment analysis method for companies’ future financial performance. It integrated the linguistic inquiry and word count method to execute the sentence segmentation and word tokenization for text retrieved from a variety of online news media and stock forums.…”

Section: Fewer Research Questions Diverse Fieldsmentioning

confidence: 99%

Deep Learning for Financial Engineering

et al. 2022

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“…Regarding profitability indicators, literature (Zhussupova, Z. et al, 2018;Bayaraa, B., 2017;Yen, M.-F. et al, 2022) most often uses ROS indicators/rates (Return on Sales), ROA (Return on Assets) and ROE (Return on Equity). The three indicators can best characterize the profitability of the entity: ROSrefers to the return on sales, ROAconsiders the return on assets held, respectively ROE -(the most important indicator from the point of view of investors) considers the return on invested capital.…”

Section: Financial Performance Analysis and Indicators (Brief Present...mentioning

confidence: 99%

Financial Performance Analysis at SC UNOTIP SRL

Blaga¹,

Zsidó

2022

Acta Marisiensis. Seria Oeconomica

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In a world where economic challenges and changes are permanent, economic entities, and especially the management of these entities, need correct and useful information to be able to make effective economic decisions regarding the present and future activity of the entities. Financial performance analysis is an indispensable and permanent managerial task, without which plans regarding future investments or strategies cannot be drawn up. The aim of the paper is to analyze the financial performance of the company SC UNOTIP SRL, based on the data from the financial statements for 5 consecutive years, with the help of the indicators that can best characterize the financial performance. Following this analysis, the authors formulate conclusions and recommendations regarding the improvement of the analyzed company’s performance.

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A Two-Dimensional Sentiment Analysis of Online Public Opinion and Future Financial Performance of Publicly Listed Companies

Cited by 13 publications

References 39 publications

Analysis of agricultural exports based on deep learning and text mining

Analysis of agricultural exports based on deep learning and text mining

Deep Learning for Financial Engineering

Financial Performance Analysis at SC UNOTIP SRL

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