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数据分析与知识发现  2020, Vol. 4 Issue (4): 63-71     https://doi.org/10.11925/infotech.2096-3467.2019.0146
  研究论文 本期目录 | 过刊浏览 | 高级检索 |
基于PreLM-FT细粒度情感分析的餐饮业用户评论挖掘
沈卓,李艳()
北京林业大学经济管理学院 北京 100083
Mining User Reviews with PreLM-FT Fine-Grain Sentiment Analysis
Shen Zhuo,Li Yan()
School of Economics and Management, Beijing Forestry University, Beijing 100083, China
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摘要 

【目的】 从大量用户评论中分析用户偏好,发现产品或服务的不足并提供改进依据。【方法】 选取大众点评网有关餐饮业的用户评论数据,对大量无监督语料进行预训练;用少量的标签数据微调预训练语言模型;对产品评论中各属性进行情感得分量化,并结合KANO模型分析用户对产品或服务的偏好。【结果】 将餐饮业用户的产品评论数据转化为用户对产品或服务的偏好。【局限】 运用KANO模型时,默认将所有用户对产品某属性的偏好视为一致,导致整体偏好分析不准确。【结论】 采用PreLM-FT细粒度情感分析,能够在仅有少量标签数据的情境下,将用户评论数据转化为用户偏好得分。

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沈卓
李艳
关键词 评论挖掘在线评论情感分析预训练语言模型    
Abstract

[Objective] This paper identifies user preferences based on their reviews of the catering providers, aiming to find and improve the un-satisfactory products or services. [Methods] Firstly, we retrieved user reviews on catering industry from the DianPing website to pre-train unsupervised corpus. Then, we fine-tuned the pre-training language model with a small amount of label data. Finally, we quantified the sentiment scores of attributes from user reviews and combined the KANO model to analyze their preferences for products or services. [Results] We successfully identified user preferences with their reviews. [Limitations] The KANO model might yield some inaccurate overall preference analysis. [Conclusions] The proposed method could effectively reveal user preferences with the help of reviews and some label data.

Key wordsReview Mining    Online Review    Sentiment Analysis    Pre-training Language Model
收稿日期: 2019-02-11      出版日期: 2020-06-01
ZTFLH:  C931.6  
通讯作者: 李艳     E-mail: liyan88@bjfu.edu.cn
引用本文:   
沈卓,李艳. 基于PreLM-FT细粒度情感分析的餐饮业用户评论挖掘[J]. 数据分析与知识发现, 2020, 4(4): 63-71.
Shen Zhuo,Li Yan. Mining User Reviews with PreLM-FT Fine-Grain Sentiment Analysis. Data Analysis and Knowledge Discovery, 2020, 4(4): 63-71.
链接本文:  
https://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/10.11925/infotech.2096-3467.2019.0146      或      https://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/Y2020/V4/I4/63
Fig.1  研究框架
Fig.2  ELMo微调基本框架
项目 总数量 类别数
训练集 105 000 20(带标签)
验证集 15 000 20(带标签)
测试集A 15 000 20(需要预测)
测试集B 200 000 20(需要预测)
Table 1  数据集数量
模型 F1
fastText 0.545
CNN 0.668
ATAE-LSTM 0.680
GCAE 0.706
Table 2  基线系统实验结果
训练数据量 模型 微调LM 不微调LM
ELMo 0.408 0.350
1 000条数据 ULMFiT 0.463 0.432
BERT 0.498 0.471
ELMo 0.598 0.410
10 000条数据 ULMFiT 0.628 0.512
BERT 0.687 0.567
ELMo 0.623 0.497
20 000条数据 ULMFiT 0.665 0.536
BERT 0.700 0.631
Table 3  预训练语言模型微调结果F1值
Fig.3  各模型在不同样本集上的F1值
模型 训练轮数
基线模型 30
ELMo 10
ULMFiT 10
BERT 5
Table 4  各模型达到最优F1值需要的最少训练轮数
属性 Xi Yi 属性 Xi Yi
菜品-外观 0.629 656 0.489 519 位置-交通是否便利 0.845 822 0.466 609
菜品-分量 0.377 323 0.455 634 其他-本次消费感受 0.597 486 0.673 873
菜品-推荐程度 0.671 923 0.484 807 其他-再次消费的意愿 0.725 646 0.768 228
菜品-口感 0.529 969 0.398 278 价格-性价比 0.639 418 0.495 803
环境-卫生情况 0.652 665 0.637 599 价格-折扣力度 0.448 763 0.480 629
环境-装修情况 0.741 353 0.425 082 价格-价格水平 0.062 375 0.563 576
环境-嘈杂情况 0.636 749 0.574 568 服务-是否容易停车 0.392 034 0.501 802
环境-就餐空间 0.481 213 0.474 076 服务-点菜/上菜速度 0.148 515 0.525 192
位置-距离商圈远近 0.912 399 0.478 381 服务-排队等候时间 0.132 728 0.518 881
位置-是否容易寻找 0.588 691 0.472 365 服务-服务人员态度 0.528 419 0.518 490
Table 5  各属性满意度
Fig.4  各属性平均满意度的散点图
属性 ri IRadj Ii 属性 ri IRadj Ii
位置-距离商圈远近 1.030 2 1.191 1 0.864 9 价格-价格水平 0.567 0 1.359 5 0.417 1
其他-再次消费的意愿 1.056 8 1.353 8 0.780 6 位置-是否容易寻找 0.754 8 1.935 4 0.390 0
位置-交通是否便利 0.966 0 1.354 6 0.713 1 服务-服务人员态度 0.740 3 1.915 7 0.386 4
环境-卫生情况 0.912 4 1.547 7 0.589 5 环境-就餐空间 0.675 5 2.101 1 0.321 5
其他-本次消费感受 0.900 6 1.578 8 0.570 4 服务-排队等候时间 0.535 6 1.676 3 0.319 5
环境-嘈杂情况 0.857 7 1.649 1 0.520 1 服务-点菜/上菜速度 0.545 8 1.714 8 0.318 3
环境-装修情况 0.854 6 1.685 3 0.507 1 价格-折扣力度 0.657 6 2.113 1 0.311 2
菜品-推荐程度 0.828 6 1.735 1 0.477 5 服务-是否容易停车 0.636 8 2.078 3 0.306 4
价格-性价比 0.809 1 1.780 2 0.454 5 菜品-口感 0.662 9 2.327 7 0.284 8
菜品-外观 0.797 6 1.813 0 0.439 9 菜品-分量 0.591 6 2.241 5 0.263 9
Table 6  产品各属性的用户满意程度
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