Version 1
: Received: 14 August 2024 / Approved: 15 August 2024 / Online: 15 August 2024 (16:35:34 CEST)
How to cite:
Reis, J. Enhancing Python-based Sentiment Analysis: Empowering Industrial Engineers and Managers in the Service Industry. Preprints2024, 2024081131. https://doi.org/10.20944/preprints202408.1131.v1
Reis, J. Enhancing Python-based Sentiment Analysis: Empowering Industrial Engineers and Managers in the Service Industry. Preprints 2024, 2024081131. https://doi.org/10.20944/preprints202408.1131.v1
Reis, J. Enhancing Python-based Sentiment Analysis: Empowering Industrial Engineers and Managers in the Service Industry. Preprints2024, 2024081131. https://doi.org/10.20944/preprints202408.1131.v1
APA Style
Reis, J. (2024). Enhancing Python-based Sentiment Analysis: Empowering Industrial Engineers and Managers in the Service Industry. Preprints. https://doi.org/10.20944/preprints202408.1131.v1
Chicago/Turabian Style
Reis, J. 2024 "Enhancing Python-based Sentiment Analysis: Empowering Industrial Engineers and Managers in the Service Industry" Preprints. https://doi.org/10.20944/preprints202408.1131.v1
Abstract
A single paragraph of about 200 words maximum. For research articles, abstracts should give a pertinent overview of the work. We strongly encourage authors to use the following style of structured abstracts, but without headings: (1) Background: Place the question addressed in a broad context and highlight the purpose of the study; (2) Methods: briefly describe the main methods or treatments applied; (3) Results: summarize the article’s main findings; (4) Conclusions: indicate the main conclusions or interpretations. The abstract should be an objective representation of the article, and it must not contain results that are not presented and substantiated in the main text and should not exaggerate the main conclusions. Python, a versatile programming language, holds vast potential for Sentiment Analysis (SA). Leveraging the Requests and TextBlob libraries, we have crafted a user-friendly code that enables Industrial Engineers (IE) and managers, particularly those who are new to Python, to extract and analyze sentiment efficiently. We aim to provide IE/managers in service companies requiring SA capabilities with a simple yet effective Python solution. While machine learning resources like PyTorch/TensorFlow are commonly utilized in SA, offering pre-built algorithms and tools for training, and implementing machine learning models, we sought to exploit Python's versatility by integrating additional web-scraping libraries. Thus, by using a lexicon-based approach, we intend to deliver an informative and practical article. The code in this article is twofold; firstly, it can be easily adapted by IE/managers possessing basic Python skills; secondly, we aim to inspire junior IE/managers to develop their own customized coding solutions tailored to specific organizational needs.
Engineering, Industrial and Manufacturing Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
