社会化在线评论情感因素测量与有用性分析

Analysis on Usefulness and Measurement of Emotion Factors in Social Online Reviews

[目的/意义]社会化在线评论与传统的专业性评论相比,具有更为显著的传播速度和影响力。文本评论中的情感因素并非单纯的数量化评分能够完全体现的。对本文评论中情感因素的测量与分析,能够有助于在线评论的全角度识别与揭示,更加客观准确地反映在线评论的价值。[过程/方法]通过提取用户发布的在线文本评论数据,采用有监督机器学习的算法,分别计算文本评论的情感分类得分、情感倾向得分、综合情感得分。从类型、地区、人数多个维度对情感得分与总评分进行交叉对比分析。[结果/结论]研究结果表明,文本评论蕴含的情感因素对总评分具有部分的影响作用。用户的认知偏好、社会文化背景和评论人数占比会对情感因素的有用性产生影响。

[Purpose/Significance] Social online reviews have more prominence propagation spread and influence than traditional professional reviews. Emotional factors in text comments cannot be fully reflected by mere quantification scores. The measurement and analysis of the emotional factors in text comments can help full angle identify and reveal the online commentary,and reflect the values of online reviews more objectively and accurately. [Process/Method] By extracting the user’ s text comment data,an algorithm of supervised machine learning was used to calculate the emotion classification score,emotion tendency score and comprehensive emotion score of text comment respectively. The emotion score and total score were cross-comparison analyzed from multiple dimensions of genre,area,number of participants. [Results/Conclusions] The results showed that the emotional factors contained in the text comments had a partial influence on the total score. User’ s cognitive preferences,social cultural background and the proportion of commentators would affect the usefulness of emotional factors.