Online comments of multi-category commodities based on emotional tendency analysis

As the promotion competition of comprehensive e-commerce platforms becomes increasingly keener, this paper aims at finding the differences and key elements of consumer perceptions of different products from the perspective of category subdivision, and exploring the transformation mechanism between text comments and graded comments in order for accurate recommendation of products. First online commodities were generally divided into six categories; and the dictionary-based method was employed to calculate the emotional distribution of each category; then the key factors affecting user experience were identified through word frequency analysis; next, by adjusting emotion intensity and emotion weights, the correlation between text comment and graded comment was studied; finally, the prediction model was built for grade correction. Significant differences exist in the emotional perceptions of consumers whose concerns have similar dimensions but different degrees. Text comments are correlated with graded comments, but deviation between the two occurs with external interference. Adjustment of emotion intensity and emotion weight has an impact on the comprehensive emotion value of products, based on which the recommendation sequencing can be optimized.