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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.
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