基于At-LSTM的产品创新特征识别

doi:10.12068/j.issn.1005-3026.2022.10.019

东北大学学报（自然科学版） ›› 2022, Vol. 43 ›› Issue (10): 1506-1512.DOI: 10.12068/j.issn.1005-3026.2022.10.019

基于At-LSTM的产品创新特征识别

闫康¹，黄训江¹，张强²，王登¹

(1. 东北大学工商管理学院，辽宁沈阳110169； 2. 安徽美芝制冷设备有限公司，安徽合肥230088)

修回日期:2021-10-19 接受日期:2021-10-19 发布日期:2022-11-07
通讯作者: 闫康
作者简介:闫康(1998-)，男，陕西西安人，东北大学硕士研究生；黄训江(1977-)，男，山东沂水人，东北大学副教授，博士生导师.
基金资助:
国家社会科学基金资助项目(20BGL044); 中央高校基本科研业务费专项资金资助项目(N2106012).

Identification of Product Innovation Features Based on At-LSTM

YAN Kang¹， HUANG Xun-jiang¹， ZHANG Qiang²， WANG Deng¹

1. School of Business Administration， Northeastern University， Shenyang 110169， China; 2. Anhui Meizhi Refrigeration Equipment Ltd.， Co.， Hefei 230088， China.

Revised:2021-10-19 Accepted:2021-10-19 Published:2022-11-07
Contact: HUANG Xun-jiang
About author:-
Supported by:
-

摘要/Abstract

摘要： 针对在线评论信息挖掘领域，既有研究尚存在上下文信息缺乏、重要内容捕获不足、噪音大、多是文本级粗粒度情感分析等问题，设计了基于注意力机制的LSTM(long short term memory)产品创新特征识别流程框架模型.通过有用性评论的筛选、特征词库和情感词库的构建、At-LSTM情感分析模型的构建及细粒度特征情感与Kano模型的结合，为企业产品的创新改进提供了明确方向.京东、淘宝购物平台有关智能手机评论的实验表明，At-LSTM模型的准确率、精确率和召回率分别为91.52%，91.73%，91.53%，相较KNN，NB，SVM等模型均有提升，产品特征不同需求层次的划分也有利于手机产品的创新改进.

关键词: 特征提取；LSTM；情感分类；Kano模型

Abstract: In view of the lack of contextual information， insufficient capture of important contents， big noise， and text-level coarse-grained sentiment analysis in the previous research of the online review information mining， a framework model of LSTML (long short term memory) product innovation feature identification based on attention mechanism was presented. Through the screening of reviews available， the construction of product feature lexicon and emotion lexicon， the construction of At-LSTM (attention based LSTM) sentiment analysis model， and the combination of fine-grained emotions and the Kano model， the clear directions for product improvement were provided. The experiments on smart phone reviews on JD.com and Taobao showed that the accuracy， precision， and recall rates of the At-LSTM model are 91.52%， 91.73% and 91.53%， respectively， which are significantly improved compared with the other models such as KNN， NB and SVM. The classification of different demand levels of product features also facilitates the innovation and improvement of mobile phone products.

Key words: feature extraction; LSTM (long short term memory); sentiment classification; Kano model

中图分类号:

TP20

闫康，黄训江，张强，王登. 基于At-LSTM的产品创新特征识别[J]. 东北大学学报（自然科学版）, 2022, 43(10): 1506-1512.

YAN Kang， HUANG Xun-jiang， ZHANG Qiang， WANG Deng. Identification of Product Innovation Features Based on At-LSTM[J]. Journal of Northeastern University(Natural Science), 2022, 43(10): 1506-1512.

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基于At-LSTM的产品创新特征识别

Identification of Product Innovation Features Based on At-LSTM

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