A Sentiment Classification Method for Text Comments Based on User Personality and Semantic-Structural Features

WANG You-wei; LIU Rui; FENG Li-zhou

doi:10.12263/DZXB.20220645

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A Sentiment Classification Method for Text Comments Based on User Personality and Semantic-Structural Features

PAPERS | 更新时间：2025-12-11

- A Sentiment Classification Method for Text Comments Based on User Personality and Semantic-Structural Features
- ACTA ELECTRONICA SINICA Vol. 52, Issue 5, Pages: 1657-1669(2024)
- 作者机构：
  
  1.中央财经大学信息学院，北京 100081
  2.天津财经大学统计学院，天津 300222
- 作者简介：
- 基金信息：
  
  National Natural Science Foundation of China(61906220);the Ministry of Education of Humanities and Social Science Project(19YJCZH178);National Social Science Foundation of China(18CTJ008);the Emerging Interdisciplinary Project of CUFE
- DOI：10.12263/DZXB.20220645
  CLC： TP 391
- Received：02 June 2022，
  
  Revised：2022-12-29，
  
  Published：25 May 2024
- 稿件说明：
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王友卫, 刘瑞, 凤丽洲. 基于用户性格和语义-结构特征的文本评论情感分类方法[J]. 电子学报, 2024, 52(05): 1657-1669.

WANG You-wei, LIU Rui, FENG Li-zhou. A Sentiment Classification Method for Text Comments Based on User Personality and Semantic-Structural Features[J]. Acta Electronica Sinica, 2024, 52(05): 1657-1669.
王友卫, 刘瑞, 凤丽洲. 基于用户性格和语义-结构特征的文本评论情感分类方法[J]. 电子学报, 2024, 52(05): 1657-1669. DOI：10.12263/DZXB.20220645

WANG You-wei, LIU Rui, FENG Li-zhou. A Sentiment Classification Method for Text Comments Based on User Personality and Semantic-Structural Features[J]. Acta Electronica Sinica, 2024, 52(05): 1657-1669. DOI：10.12263/DZXB.20220645

摘要

由于传统文本评论情感分类方法通常忽略用户性格对于情感分类结果的影响，提出一种基于用户性格和语义-结构特征的文本评论情感分类方法（User Personality and Semantic-structural Features based Sentiment Classification Method for Text Comments，BF_BiGAC）.依据大五人格模型能够有效表达用户性格的优势，通过计算不同维度性格得分，从评论文本中获取用户性格特征.利用双向门控循环单元（Bidirectional Gated Recurrent Unit，BiGRU）和卷积神经网络（Convolutional Neural Network，CNN）可以有效提取文本上下文语义特征和局部结构特征的优势，提出一种基于BiGRU、CNN和双层注意力机制的文本语义-结构特征获取方法.为区分不同类型特征的影响，引入混合注意力层实现对用户性格特征和文本语义-结构特征的有效融合，以此获得最终的文本向量表达.在IMDB、Yelp-2、Yelp-5及Ekman四个评论数据集上的对比实验结果表明，BF_BiGAC在分类准确率（Accuracy）和加权macro

值（

）上均获得较好表现，相对于拼接BiGRU、CNN的情感分类方法（Sentiment Classification Method Concatenating BiGRU and CNN，BiGRU_CNN）在Accuracy值上分别提升0.020、0.012、0.017及0.011，相对于拼接CNN、BiGRU的情感分类方法（Sentiment Classification Method Concatenating CNN and BiGRU，ConvBiLSTM）

值上分别提升0.022、0.013、0.028及0.023；相对于预训练模型BERT和RoBERTa，BF_BiGAC在保证分类精度的情况下获得了较高的运行效率.

Abstract

Since the traditional sentiment classification methods for text comments usually ign

ore the influence of user personality on sentiment classification results

a sentiment classification method for text comments based on user personality and semantic-structural features is proposed. According to the advantage of Big Five personality model on effectively expressing the user personality

the user personality feature is obtained from the comment texts by calculating the personality scores from different dimensions. Moreover

the advantages of bidirectional gated recurrent unit (BiGRU) and convolutional neural network (CNN) on effectively extracting the contextual semantic features and the local structural features are taken

and a new text semantic-structural feature acquisition method based on BiGRU

CNN and two-layer attention mechanism is proposed. Finally

in order to distinguish the influence of the features with different types

the hybrid attention layer is introduced to obtain the final text vector representation by integrating the user personality feature and the textural semantic-structural feature effectively. The experimental results on the datasets of IMDB

Yelp-2

Yelp-5 and Ekman show that BF_BiGAC achieves good performance when the measurements of Accuracy and weighted macro

(

) are used. Specifically

it achieves the improvements of 0.020

0.012

0.017 and 0.011 compared to sentiment classification method concatenating BiGRU and CNN (BiGRU_CNN) on accuracy

and achieves the improvements of 0.022

0.013

0.028 and 0.023 compared to sentiment classification method concatenating CNN and BiGRU (ConvBiLSTM) on F

. Moreover

when comparing with the pre-trained models of BERT and RoBERTa

BF_BiGAC achieves higher executing efficiency while ensuring the classification accuracy.

关键词

Keywords

references

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