基于DSR和BGRU模型的聊天文本证据分类方法

doi:10.11959/j.issn.2096-109x.2022007

Abstract

Abstract:

It is always unlikely to efficiently identify and extract chat text evidence related to criminal events, due to the complex semantics such as “slang” in the chat content and the huge amount of chat text data generated by social software such as instant messaging.Based on this motivation, a chat text evidence classification model (DSR-BGRU) based on the DSR (dynamic semantic representation) model and the BGRU (bidirectional gated recurrent unit) model was proposed.The chat text data was pre-processed to preserve the characteristics of the criminal field.Then a multi-layer chat text feature extraction and classification model using the Keras framework was proposed.With the text matrix composed of vector representation of words in the DSR model as the input vector, the input layer of the DSR model featured the chat text from the semantic level.Then the hidden layer of the BGRU model extracted the context characteristics of the text composed of the word vectors.The softmax classification layer recognized and extracted the chat text evidence.The experimental results show that the proposed DSR-BGRU can more accurately identify and extract chat records compared with other models and methods for text classification, and it can also effectively extract the criminal text information from the chat information with the accuracy rate 92.06% and the F1 score 91.00%.

Key words: text semantic representation, polysemy, text classification, digital forensics

CLC Number:

TP391

Yu ZHANG, Binglong LI, Xuejuan LI, Heyu ZHANG. Evidence classification method of chat text based on DSR and BGRU model[J]. Chinese Journal of Network and Information Security, 2022, 8(2): 150-159.

Figures/Tables 15

References 17

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原始聊天记录	切词及词性标注后文本
有猪肉么	有/v猪肉/n么/o
有信么，我这有洗信人	有/v信/n么/o，我/o这/o有/v洗信人/n
四号到货了，要溜么	四号/n到/v货/n了/o，要/v溜/v么/o

词性	对应含义
n	名词
v	动词
o	其他

文本类型	主题
正常文本	生活
正常文本	娱乐
正常文本	学习
正常文本	体育
正常文本	财经
异常文本	色情
异常文本	毒品
异常文本	赌博

语料类别	正常文本	异常文本
训练集	8 944	10 336
测试集	2 236	2 584
合计	11 180	12 920

数据集	正面情感数量	负面情感数量	总数
ChnSentiCorp_htl_all	5 000	2 000	7 000
waimai_10k	4 000	4 000	8 000
online_shoping_10_cats	3 000	3 000	6 000
weibo_senti_100k	5 000	5 000	10 000

Evidence classification method of chat text based on DSR and BGRU model

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Figures/Tables 15

References 17

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参数名称	参数值
学习率	1×10^-3
优化器	AdaDelta
损失函数	MSE
Dropout率	0.5

模型	准确率	召回率	F1值
DSR-BGRU	92.06%	89.97%	91.00%
文献[2]	81.17%	81.83%	81.50%
文献[14]	82.72%	82.47%	82.59%
文献[15]	89.95%	89.31%	89.63%
文献[16]	91.68%	90.17%	90.92%
文献[17]	86.85%	86.43%	86.64%

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