The Potential of AI in Performing Financial Sentiment Analysis for Predicting Entrepreneur Survival
Abstract
:1. Introduction
2. Literature Review
2.1. Theoretical and Methodological Frameworks’ Competing Views on the Economic Implications of AI
2.2. Failure Predictions
- Resources-based resources: it states that the deployment of resources is an important and central element in enterprise failure. The lack of resources and competencies contributes to failure (Auken 2007). Therefore, we have asked enterprises how their sales do, gain and loss variate during the crisis, and collected text data that we have analyzed the sentiment with FinBERT. Then, we measured the correlation between the age, size, and type of the enterprise and the sentiments;
- Size and age of the enterprise: younger enterprises fail more often than the oldest (Stinchcombe 1965), cited by (Séverin and Veganzones 2023). He explains that by the fact that young enterprises lack legitimacy and resources because of the lack of expertise and knowledge on the market. These are disadvantages related to big companies that are well-established in the market.
- Type of the enterprises: failure trajectories were elaborated by many studies (Séverin and Veganzones 2023). Failure is defined as a situation where enterprises stop their activities and lose their identity because of their incapacity to adapt. However, the trajectories that are the most similar to the one that we have detected through a previous analysis that we have performed on 14 entrepreneurs (Lina and Levy-Tadjine 2023) and through the follow-up in 2024 on the same structures performed by (Crutzen and Van Caillie 2010). In this trajectory, three main company types were identified: startups having non-mastered hypergrowth, incapacity to adapt to the environment after years of growth, and enterprises that are touched by an external shock.
2.3. Corporate Sustainability
2.4. Sentiment Analysis
- Tokenization: Breaking down text into smaller units (tokens), such as words or phrases;
- Sentiment Lexicons: Using pre-built dictionaries of words associated with positive or negative sentiment;
- Machine Learning: Training models to recognize sentiment from labeled data. Common algorithms include Naive Bayes, Support Vector Machines (SVM), and neural networks;
- Deep Learning: Using advanced neural network architectures like Long Short-Term Memory (LSTM) networks and transformers for more accurate sentiment detection;
- Natural Language Processing (NLP): Techniques like part-of-speech tagging, named entity recognition, and dependency parsing to understand the structure and meaning of sentences;
- Feature Extraction: Converting text into numerical features using methods like TF-IDF (Term Frequency-Inverse Document Frequency) or word embeddings (Word2Vec, GloVe, BERT).
3. Materials and Methods
4. Results
4.1. ChatGPT-4 Compared to ChatGPT-4.0, FinBERT, and LeChat-Mistral for Sentiment Analysis
4.2. Sentiment Analysis Predicts Entreprises Sustainability
5. Discussion
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Appendix A. Sample Description
References
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Prompt | Model | Positive | Neutral | Negative | Overall Score |
---|---|---|---|---|---|
Primary Set of Prompts: (Sentence Only) | |||||
Prompt 1 | ChatGPT-4 | - | - | - | 0.5 to 0.6 |
ChatGPT-4.0 | - | - | - | 0.55 | |
LeChat-Mistral | - | - | - | 0.6 | |
Prompt 2 | ChatGPT-4 | 0.4 | 0.4 | 0.2 | - |
ChatGPT-4.0 | 0.4 | 0.2 | 0.4 | - | |
LeChat-Mistral | 0.5 | 0.5 | 0.0 | - | |
Prompt 3 | ChatGPT-4 | - | - | - | Neutral/Mixed |
ChatGPT-4.0 | 0.45 | 0.2 | 0.35 | - | |
LeChat-Mistral | - | - | - | Neutral/Mixed | |
Prompt 4 | ChatGPT-4 | - | - | - | Slightly Positive |
ChatGPT-4.0 | - | - | - | Mixed/Positive | |
LeChat-Mistral | - | - | - | Neutral/Positive | |
Prompt 5 | ChatGPT-4 | 0.6 | 0.3 | 0.1 | - |
ChatGPT-4.0 | 0.3 | 0.4 | 0.3 | - | |
LeChat-Mistral | 0.6 | 0.5 | 0.4 | - | |
Prompt 6 | ChatGPT-4 | 0.2 | 0.5 | 0.3 | - |
ChatGPT-4.0 | 0.4 | 0.2 | 0.4 | - | |
LeChat-Mistral | 0.6 | 0.2 | 0.2 | - | |
Secondary Set of Prompts (Question and Sentence Together) | |||||
Prompt 1′ | ChatGPT-4 | - | - | - | 0.5 to 0.6 |
ChatGPT-4.0 | - | - | - | 0.5 | |
LeChat-Mistral | - | - | - | 0.5 | |
Prompt 2′ | ChatGPT-4 | 0.4 | 0.5 | 0.1 | - |
ChatGPT-4.0 | 0.3 | 0.4 | 0.3 | - | |
LeChat-Mistral | 0.4 | 0.6 | 0.2 | - | |
Prompt 3′ | ChatGPT-4 | - | - | - | Slightly Positive |
ChatGPT-4.0 | - | - | - | Mixed | |
LeChat-Mistral | - | - | - | Neutral/Positive | |
Prompt 4′ | ChatGPT-4 | - | - | - | Mixed/Positive |
ChatGPT-4.0 | - | - | - | Mixed/Positive | |
LeChat-Mistral | - | - | - | Mixed/Positive | |
Prompt 5′ | ChatGPT-4 | 0.6 | 0.3 | 0.1 | - |
ChatGPT-4.0 | 0.3 | 0.4 | 0.3 | - | |
LeChat-Mistral | 0.4 | 0.5 | 0.1 | - | |
Prompt 6′ | ChatGPT-4 | 0.6 | 0.3 | 0.1 | - |
ChatGPT-4.0 | 0.4 | 0.2 | 0.4 | - | |
LeChat-Mistral | 0.4 | 0.5 | 0.1 | - |
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Share and Cite
Saleh, L.; Semaan, S. The Potential of AI in Performing Financial Sentiment Analysis for Predicting Entrepreneur Survival. Adm. Sci. 2024, 14, 220. https://doi.org/10.3390/admsci14090220
Saleh L, Semaan S. The Potential of AI in Performing Financial Sentiment Analysis for Predicting Entrepreneur Survival. Administrative Sciences. 2024; 14(9):220. https://doi.org/10.3390/admsci14090220
Chicago/Turabian StyleSaleh, Lina, and Samer Semaan. 2024. "The Potential of AI in Performing Financial Sentiment Analysis for Predicting Entrepreneur Survival" Administrative Sciences 14, no. 9: 220. https://doi.org/10.3390/admsci14090220
APA StyleSaleh, L., & Semaan, S. (2024). The Potential of AI in Performing Financial Sentiment Analysis for Predicting Entrepreneur Survival. Administrative Sciences, 14(9), 220. https://doi.org/10.3390/admsci14090220