KEYWORDS: Information technology, Computer simulations, Statistical analysis, Analytical research, Algorithm development, Distributed interactive simulations, Data analysis, Data mining, Data modeling, Data visualization, Databases
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.
KEYWORDS: Information technology, Analytical research, Education and training, Data modeling, Semantics, Performance modeling, Reflection, Design, Accuracy assessment, Data mining
Macro work is a significant Online Labor Platforms (OLPs) operation characterized by higher professionalism for service providers. Therefore, the professionalism assessment for providers of macro work is vital for OLPs. However, due to the high ambiguity of textual data, OLPs often overlook them when evaluating the Service Provider Professionalism (SPP) of macro work. Within OLPs, there is a large amount of textual data, which contains information reflecting their professionalism. Hence, this study proposes a method for evaluating the SPP of macro work on OLPs based on text sentiment analysis: (1) Select professional vocabulary related to a specific type of macro work as sentiment words; (2) Collect texts and score their professionalism values; (3) Calculate the sentiment word professionalism value based on the NBSP algorithm - an algorithm that combines the Naive Bayes and Semantic Orientation Pointwise Mutual Information (SO-PMI) algorithms; (4) Calculate the text professionalism value, namely the SPP value. Algorithm validation results show that compared to baseline algorithms, the NBSP algorithm achieves an increase in the accuracy of calculating text professionalism values by 4.45 - 27.75 percent points. To validate this method's effectiveness, this study conducted a comparative experiment on predicting the annual transaction amounts of IT service providers on a certain Chinese OLP under eight main-stream predictive models, incorporating the feature of SPP reduced MSE by 6% - 12%. This study contributes to expanding research in structuring textual data and text sentiment analysis in OLPs and enhances professionalism assessment for service providers of macro work on OLPs.
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