计算机科学 ›› 2021, Vol. 48 ›› Issue (11A): 251-257.doi: 10.11896/jsjkx.201200202
王友卫1, 朱晨1, 朱建明1, 李洋1, 凤丽洲2, 刘江淳1
WANG You-wei1, ZHU Chen1, ZHU Jian-ming1, LI Yang1, FENG Li-zhou2, LIU Jiang-chun1
摘要: 微博是一个可以分享生活、发表看法、发泄情感的社交平台,由于数据量大且易于获取,微博数据已被广泛用于网络用户情感分析。传统对微博进行情感预测的研究没有考虑用户的用词喜好、语言风格等个性化因素的影响,使得情感分类结果的准确性不高。首先通过分析用户兴趣特征构建用户兴趣词典,在此基础上提出基于用户兴趣词典的情感分类模型;然后利用长短期记忆网络(Long Short-Term Memory,LSTM)分类准确性高的特点训练一个通用的LSTM分类模型;最后利用支持向量机融合不同模型以得到最终的情感分类结果。实验结果表明,与支持向量机、朴素贝叶斯等传统分类器相比,基于用户兴趣词典与LSTM的个性化情感分类方法在分类精度上有较大提升;与LSTM、循环神经网络等深度学习方法相比,该方法在保证运行效率的前提下能获得更高的分类精度。
中图分类号:
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