Mr. Hongbin Zhang Profile

Hongbin Zhang

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Publications (2)

Proceedings Article | 5 July 2024 Paper

Using the equal sentiment enhancement with distribution (ESED) algorithm in text sentiment analysis: predicting customers purchasing intention (CPI) for IT services on freelance platforms

Hongbin Zhang, Linyu Wang, Jiajun Xu

Proceedings Volume 13184, 131841G (2024) https://doi.org/10.1117/12.3032901

KEYWORDS: Information technology, Computer simulations, Statistical analysis, Analytical research, Algorithm development, Distributed interactive simulations, Data analysis, Data mining, Data modeling, Data visualization, Databases

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Text sentiment analysis is a mainstream method in text information analysis, which could extract important data information from text, including customer needs and opinions on the product or service. Mainstream algorithms used in text sentiment analysis typically require large amounts of data to train models, making them less applicable in research areas with limited datasets, such as information technology (IT) services. Therefore, this paper proposes a text sentiment analysis algorithm specifically designed for small sample datasets—the Equal Sentiment Enhancement with Distribution (ESED) algorithm. The kernel of this algorithm is the improvement and innovation of the gradient descent algorithm in sentiment analysis with small sample datasets. This algorithm only requires that the number of mutually independent texts exceed the number of sentiment words, and it could achieve relatively accurate calculation of word sentiment values even under the condition of small sample datasets. The present study conducts simulation experiments to illustrate the convergence and usage condition of the ESED algorithm, demonstrating its feasibility in the application of text sentiment analysis with small sample datasets. To demonstrate the superiority of the ESED algorithm in text sentiment analysis with small sample datasets, it selects the problem of customers' purchase intention (CPI) prediction for IT services on freelance platforms for comparative experiments, comparing the performance of the ESED algorithm with four mainstream text sentiment analysis algorithms. Comparative experiment results show that compared to four baseline algorithms, the ESED algorithm achieves a decrease in Mean Squared Error (MSE) of predicting the CPI by 18.0%–51.5% in the dataset with 1196 samples. Conclusively, this paper contributes to the extended application of text-based sentiment analysis research in the field of research with small sample datasets and improves the prediction of CPI for IT services on freelance platforms.

Proceedings Article | 6 June 2024 Paper

Text-based sentiment analysis for evaluating the service provider professionalism (SPP) of macro work on online labor platforms (OLPs)

Hongbin Zhang, Jiajun Xu

Proceedings Volume 13175, 131750O (2024) https://doi.org/10.1117/12.3031905

KEYWORDS: Information technology, Analytical research, Education and training, Data modeling, Semantics, Performance modeling, Reflection, Design, Accuracy assessment, Data mining

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