计算机科学 ›› 2021, Vol. 48 ›› Issue (2): 87-92.doi: 10.11896/jsjkx.200700111
所属专题： 大数据&数据科学 虚拟专题
李可悦1, 陈轶2, 牛少彰1
LI Ke-yue1, CHEN Yi2, NIU Shao-zhang1
摘要： 随着网络购物的高速发展,网络商家和购物者在网络交易活动中产生了大量的交易数据,其中蕴含着巨大的分析价值。针对社交电商商品文本的文本分类问题,为了更加高效准确地判断文本所描述商品的类别,提出了一种基于BERT模型的社交电商文本分类算法。首先,该算法采用BERT(Bidirectional Encoder Representations from Transformers)预训练语言模型来完成社交电商文本的句子层面的特征向量表示,随后有针对性地将获得的特征向量输入分类器进行分类,最后采用社交电商文本的数据集进行算法验证。实验结果表明,经过训练的模型在测试集上的分类结果F1值最高可达94.61%,高出BERT模型针对MRPC的分类任务6%。因此,所提社交电商文本分类算法能够较为高效准确地判断文本所描述商品的类别,有助于进一步分析网络交易数据,从海量数据中提取有价值的信息。
中图分类号:
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