Overview of Financial Applications for Investing on the Stock Exchange - Regression Models and Sentiment Analysis

Probierz, Eryka; Gałuszka, Adam; Grzejszczak, Tomasz; Jędrasiak, Karol; Wiśniewski, Tomasz; Dzida, Tomasz

doi:10.35808/ersj/2860

Cited by 1 publication

(1 citation statement)

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“…According to positive (negative) polarity, there are more positive (negative) tweet sentiments than negative (positive) tweet sentiments (Mohbey et al , 2022; Priyadarshini et al , 2022; Praveen et al , 2021). The VADER, a lexicon and rule-based sentiment analysis written in Python, was used to determine each tweet’s sentiment score(Hossain and Rahman, 2022; Probierz et al , 2022). Further traditional ML models [like the random forest (RF), logistic regression (LR), support vector machine (SVM), XGBoost and Naive Bayes (NB)], Neural networks (bidirectional LSTM) and Attention base model (RoBERTa) were experimented with to get the best sentiment accuracy and predict future tweet’s sentiment in a real-time basis.…”

Section: Methodsmentioning

confidence: 99%

Decoding mood of the Twitterverse on ESG investing: opinion mining and key themes using machine learning

Jaiswal,

Gupta,

Tiwari

2024

MRR

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Purpose Grounded in the stakeholder theory and signaling theory, this study aims to broaden the research agenda on environmental, social and governance (ESG) investing by uncovering public sentiments and key themes using Twitter data spanning from 2009 to 2022. Design/methodology/approach Using various machine learning models for text tonality analysis and topic modeling, this research scrutinizes 1,842,985 Twitter texts to extract prevalent ESG investing trends and gauge their sentiment. Findings Gibbs Sampling Dirichlet Multinomial Mixture emerges as the optimal topic modeling method, unveiling significant topics such as “Physical risk of climate change,” “Employee Health, Safety and well-being” and “Water management and Scarcity.” RoBERTa, an attention-based model, outperforms other machine learning models in sentiment analysis, revealing a predominantly positive shift in public sentiment toward ESG investing over the past five years. Research limitations/implications This study establishes a framework for sentiment analysis and topic modeling on alternative data, offering a foundation for future research. Prospective studies can enhance insights by incorporating data from additional social media platforms like LinkedIn and Facebook. Practical implications Leveraging unstructured data on ESG from platforms like Twitter provides a novel avenue to capture company-related information, supplementing traditional self-reported sustainability disclosures. This approach opens new possibilities for understanding a company’s ESG standing. Social implications By shedding light on public perceptions of ESG investing, this research uncovers influential factors that often elude traditional corporate reporting. The findings empower both investors and the general public, aiding managers in refining ESG and management strategies. Originality/value This study marks a groundbreaking contribution to scholarly exploration, to the best of the authors’ knowledge, by being the first to analyze unstructured Twitter data in the context of ESG investing, offering unique insights and advancing the understanding of this emerging field.

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