Method for Automatically Generating Online Comments

被引：0
|
作者：
Liu X. [1 ,2 ]
Xu Y. [1 ,2 ]
Li J. [3 ]
机构：
[1] Beijing Key Laboratory of Network Culture and Digital Communication, Beijing University of Information Science and Technology, Beijing
[2] School of Computer Science, Beijing University of Information Science and Technology, Beijing
[3] School of Humanities and Education, Beijing Open University, Beijing
来源：
Data Analysis and Knowledge Discovery | 2023年 / 7卷 / 04期
基金：
中国国家自然科学基金;
关键词：
Seq2Seq; SeqGAN; Social Network Comment Posts; TCN;
D O I：
10.11925/infotech.2096-3467.2022.0379
中图分类号：
学科分类号：
摘要：
[Objective] This paper proposes a Temporal Sequence Generative Adversarial Network (T-SeqGAN) automatically generating online comments, aiming to counteract malicious information on social networks and guide the correct direction of public opinion. [Methods] First, we modified the Sequence Generative Adversarial Network (SeqGAN) generator to a Seq2Seq structure. Then, we used the bidirectional gated recurrent unit (BiGRU) and the sequential convolutional neural network (TCN) as the skeleton network of the encoder and decoder, respectively. Next, we improved the similarity of the syntactic structure and semantic features between the generated posts and the real online comments. Finally, we modified the discriminator of SeqGAN to a model combing TCN and attention mechanism layers to improve the fluency of generated posts. [Results] Compared with the baseline model, the comments generated by the proposed model have significantly higher BLEU-2 (0.799 35), BLEU-3(0.603 96), BLEU-4(0.476 42), and KenLM (-27.670 29)metrics, as well as lower PPL (0.752 47) metrics. [Limitations] The vocabulary and language style of the generated posts are limited by actual posts, and the applicability of our method is limited. [Conclusions] The comments generated by the proposed model have higher syntactic and grammatical correctness and higher similarity to the real-world ones, which can guide the correct direction of public opinion on social networks. © 2023 Data Analysis and Knowledge Discovery. All rights reserved.
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页码：101 / 113
页数：12
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