• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Information Processing & Management >Multilingual opinion mining on YouTube - A convolutional N-gram BiLSTM word embedding
【24h】

Multilingual opinion mining on YouTube - A convolutional N-gram BiLSTM word embedding

机译：YouTube上的多语言意见挖掘-卷积N-gram BiLSTM单词嵌入

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Opinion mining in a multilingual and multi-domain environment as YouTube requires models to be robust across domains as well as languages, and not to rely on linguistic resources (e.g. syntactic parsers, POS-taggers, pre-defined dictionaries) which are not always available in many languages. In this work, we i) proposed a convolutional N-gram BiLSTM (CoNBiLSTM) word embedding which represents a word with semantic and contextual information in short and long distance periods; ii) applied CoNBiLSTM word embedding for predicting the type of a comment, its polarity sentiment (positive, neutral or negative) and whether the sentiment is directed toward the product or video; iii) evaluated the efficiency of our model on the SenTube dataset, which contains comments from two domains (i.e. automobile, tablet) and two languages (i.e. English, Italian). According to the experimental results, CoNBiLSTM generally outperforms the approach using SVM with shallow syntactic structures (STRUCT) – the current state-of-the-art sentiment analysis on the SenTube dataset. In addition, our model achieves more robustness across domains than the STRUCT (e.g. 7.47% of the difference in performance between the two domains for our model vs. 18.8% for the STRUCT).
机译：YouTube要求在多语言和多域环境中进行意见挖掘，因此模型必须跨域和语言都具有稳健性，并且不依赖于并非总是可用的语言资源（例如语法解析器，POS语法，预定义词典）在许多语言中。在这项工作中，我们i）提出了卷积N-gram BiLSTM（CoNBiLSTM）词嵌入，该词嵌入表示具有短时和长距离周期的语义和上下文信息的词； ii）应用CoNBiLSTM词嵌入来预测评论的类型，其极性情感（正面，中性或负面）以及情感是针对产品还是视频； iii）在SenTube数据集上评估了我们模型的效率，该数据集包含来自两个域（即汽车，平板电脑）和两种语言（即英语，意大利语）的注释。根据实验结果，CoNBiLSTM通常优于使用具有浅句法结构（STRUCT）的SVM（SenTube数据集上的最新技术分析）的方法。此外，与STRUCT相比，我们的模型在各个域上均具有更高的鲁棒性（例如，模型的两个域之间的性能差异为7.47％，而STRUCT则为18.8％）。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号