Theoretical and Natural Science
- The Open Access Proceedings Series for Conferences
Theoretical and Natural Science
Vol. 34, 02 April 2024
Open
Access | Article
An enhancement methodology for predicting transaction amounts for freelancers on freelance platforms: Based on sentiment analysis
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Abstract
Freelancing is a type of labor arrangement in which independent freelancers use their discretionary time to perform ad hoc tasks, usually of a single nature, with the aim of getting paid. This study proposes an augmented method for predicting the amount of freelance transactions using textual sentiment analysis. First, a unique feature labeled “Freelancer Sentiment” was selected to summarize the positive or negative sentiment orientation of freelancers. Subsequently, Naive Bayes algorithm is applied to process the text data from the freelancer’s platform to finally develop a “Model for Computing Word Sentiment Values”. The model helps to accurately calculate the sentiment values associated with emotional words. Finally, the Word Frequency-Inverse Document Frequency (TF-IDF) algorithm is used to construct the Text Sentiment Value Calculation Model, so as to accurately calculate the sentiment values of freelancers. The results of the comparison experiments of the five commonly used prediction models show that the mean squared error (MSE) of the model that includes the “freelancer sentiment” feature is significantly reduced by 6%-11% compared with the model that does not include the “freelancer sentiment” feature. This study contains theoretical explorations and practical implications. First, the proposed approach of extracting features from textual data to build predictive models provides a valuable reference for future enhancement of predictive modeling on freelance platforms, especially those that rely on unstructured data. Second, incorporating textual sentiment value features relevant to freelancers can significantly improve the accuracy of predicting transaction amounts. Third, the calculation of word and text sentiment values employs a series of algorithms that target specific features of the freelancer platform’s text data. This approach is important for improving the accuracy of feature value calculation.
Keywords
freelance platforms, sentiment analysis, feature extraction, Naive Bayes algorithm
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Data Availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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- Volume Title
- Proceedings of the 3rd International Conference on Computing Innovation and Applied Physics
- ISBN (Print)
- 978-1-83558-369-2
- ISBN (Online)
- 978-1-83558-370-8
- Published Date
- 02 April 2024
- Series
- Theoretical and Natural Science
- ISSN (Print)
- 2753-8818
- ISSN (Online)
- 2753-8826
- DOI
- 10.54254/2753-8818/34/20241167
- Copyright
- 02 April 2024
- Open Access
- This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited