【目的】為提高產品選擇效率,幫助消費者更好地制定購物決策,本文在門限遞歸單元的基礎上,提出一種特征強化雙向門限遞歸單元模型(Feature Bidirectional Gated Recurrent Unit,F-Bi GRU)。【方法】首先,獲取相關產品的在線評論信息;然后對在線評論按照產品屬性進行分割;使用正向情感評論和負向情感評論對F-Bi GRU模型進行訓練;最后使用F-Bi GRU模型對產品各屬性的評論進行情感量化,得到產品各屬性的情感滿意程度,并使用TOPSIS法對候選產品進行排序。【結果】選取汽車口碑文本評論數據進行實證,對比相關情感分析方法,F-Bi GRU方法提高了情感分析的準確度,更適應在線評論短文本的特點。【局限】深度學習模型需要大規模的數據集,本文方法在一些小數據集上的表現可能不佳。【結論】基于F-Bi GRU情感分析的產品選擇方法提高了情感分析的準確度,能更高效快捷地幫助消費者進行產品選擇。

[Objective] This paper proposes a product selection method based on the Feature Bidirectional Gated Recurrent Unit model(F-Bi GRU), aiming to improve the efficiency of customers’ product selection and help them make better shopping decisions. [Methods] First, we retrieved online reviews for related products. Then, we categorized these online reviews in accordance with the product attributes. Third, we trained the F-Bi GRU model using positive and negative reviews. Fourth, we quantified the sentiment of reviews on different attributes with the F-Bi GRU model. Finally, we got the degrees of satisfaction on product attributes, and sorted the products using TOPSIS method. [Results] We retrieved the review texts on cars to conduct an empirical analysis. We found that the F-Bi GRU method improved the accuracy of sentiment analysis, and is more appropriate for the short text reviews than traditional methods. [Limitations] The proposed deep learning model requires large dataset, which limits its performance with smaller datasets. [Conclusions] The product selection method based on F-Bi GRU helps consumers choose needed products more efficiently.