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新疆大学信息科学与工程学院
Published:2020
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[1]艾山·吾买尔,魏文琳,早克热·卡德尔.基于BiLSTM+Attention的体育领域情感分析研究[J].新疆大学学报(自然科学版)(中英文),2020,37(02):142-149.
[1]艾山·吾买尔,魏文琳,早克热·卡德尔.基于BiLSTM+Attention的体育领域情感分析研究[J].新疆大学学报(自然科学版)(中英文),2020,37(02):142-149. DOI: 10.13568/j.cnki.651094.651316.2019.12.06.0001.
DOI:10.13568/j.cnki.651094.651316.2019.12.06.0001.
针对体育领域情感分析资源不足、分析性能不高的现状
对体育领域的情感分析开展了研究.首先从"新浪体育"和"直播吧"等平台经过人工筛选、标注
构建了中文情感标注语料库CH-SPORT
共标记评论10 000条
其中积极评论5 000条
消极评论5 000条.然后选用了SVM、TextCNN、BiLSTM、RCNN、fastText、BiLSTM+Attention等模型对CH-SPORT进行了评估.实验结果表明
BiLSTM+Attention模型在CH-SPORT上的分类效果最佳
Acc为87.75%
比基准数据集ChnSentiCorp和NLPCC2014分别高出18.65%、11.75%.本文构建的数据集能有效应用于体育情感分析研究中.
At present
the research on sentiment analysis in sports field and publicly available corpus resources are very rare and low effect of performance.Therefore
this paper construct an emotional annotation corpus for sports.Firstly
the corpus resources come from "Sina Sports Network" and "Live Bar".After data preprocessing
the emotional polarity annotation is manually performed and then different algorithms and external dataset are used for analysis and comparison.A total of 10 000 comments were marked
including 5 000 positive comments and5 000 negative comments.Then
SVM
TextCNN
BiLSTM
RCNN
fast Text
BiLSTM+Attention were selected to evaluate CH-SPORT.The experimental results show that BiLSTM+Attention model on CH-SPORT is the best
the accuracy can reach 87.75%
18.65% and 11.75% higher than the benchmark datasets ChnSentiCorp and NLPCC2014
respectively
the B:LSTM+Attention model can effectively improve the classification effect.The corpus constructed in this paper can be effectively used in sports sentiment analysis.
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