A MABM-based Model for Identifying Consumers' Sentiment Polarity ―Taking Movie Reviews as an Example

Liu Hong-wei; Lin Wei-zhen; Wen Zhan-ming; Chen Yan-jun; Yi Min-qi

doi:10.12052/gdutxb.220123

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A MABM-based Model for Identifying Consumers' Sentiment Polarity ―Taking Movie Reviews as an Example

更新时间：2026-05-27

- A MABM-based Model for Identifying Consumers' Sentiment Polarity ―Taking Movie Reviews as an Example
- Vol. 39, Issue 6, Pages: 1-9(2022)
- 作者机构：
  
  广东工业大学管理学院,广东,广州,510520
- 作者简介：
- 基金信息：
- DOI：10.12052/gdutxb.220123
  CLC： TP319.4;F713.8
- Published：2022
- 稿件说明：
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刘洪伟, 林伟振, 温展明, 陈燕君, 易闽琦. 基于MABM的消费者情感倾向识别模型——以电影评论为例[J]. 广东工业大学学报, 2022, 39(6): 1-9.

Liu Hong-wei, Lin Wei-zhen, Wen Zhan-ming, et al. A MABM-based Model for Identifying Consumers' Sentiment Polarity ―Taking Movie Reviews as an Example[J]. 2022, 39(6): 1-9.
刘洪伟, 林伟振, 温展明, 陈燕君, 易闽琦. 基于MABM的消费者情感倾向识别模型——以电影评论为例[J]. 广东工业大学学报, 2022, 39(6): 1-9. DOI： 10.12052/gdutxb.220123.

Liu Hong-wei, Lin Wei-zhen, Wen Zhan-ming, et al. A MABM-based Model for Identifying Consumers' Sentiment Polarity ―Taking Movie Reviews as an Example[J]. 2022, 39(6): 1-9. DOI： 10.12052/gdutxb.220123.

摘要

识别线上消费者群体评论的情感倾向，有助于优化平台推荐算法及提升服务质量，如何有效识别消费者情感倾向，是一个热门的研究选题。本文基于多头自注意力机制的双向长短期机制提出MABM(Multi-head self-Attention and Bidirectional long-short term Memory neural network)情感倾向识别模型，采用知名电影点评网站豆瓣点评在线评论数据作为语料，使用文本挖掘工具对数据进行预处理，以10个机器学习模型和4个深度学习模型为对照组，按照8:2划分训练集和测试集来验证对比评估MABM模型的有效性和稳健性。两组对比实验结果发现，深度神经网络模型预测效果整体优于机器学习模型，并且以MABM模型的分类效果最佳。MABM模型能够有效识别消费者评论的情感倾向，使推荐算法能有效结合消费者的心理行为，以获得更显著的营销效果。

Abstract

Identifying the sentiment polarity of movie customer group reviews may inspire the platform to optimize movie recommendation algorithms and improve services and provide suggestions for consumers' movie choices. A MABM sentiment polarity recognition model is proposed based on the multi-head self-attention bidirectional long-short term mechanism. Using the online review data of the well-known movie review website Douban Review as the corpus

text mining tools are used to pre-process the data. Then dividing the data into training and test sets according to 8:2

and using 10 machine learning models and 4 deep learning models as the control group

the effectiveness and robustness of the MABM model are evaluated by cross-validation and test set validation comparison. In the two sets of comparison experiments

it is found that the deep neural network model predicts overall better than the machine learning model

and the MABM model used as the control set has the best classification results. The MABM model can effectively identify the sentiment polarity of consumer reviews

providing management insights and algorithm improvement suggestions for movie recommendation platforms.

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Keywords

references

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