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A novel category detection of social media reviews in the restaurant industry
Multimedia Systems ( IF 3.5 ) Pub Date : 2020-10-24 , DOI: 10.1007/s00530-020-00704-2
Mohib Ullah Khan , Abdul Rehman Javed , Mansoor Ihsan , Usman Tariq

Social media platforms have enabled users to share their thoughts, ideas, and opinions on different subject matters and meanwhile generate lots of information which can be adopted to understand people’s emotion towards certain products. This information can be effectively applied for Aspect Category Detection (ACD). Similarly, people’s emotions and recommendation-based Artificial Intelligence (AI)-powered systems are in trend to assist vendors and other customers to improve their standards. These systems have applications in all sorts of business available on multiple platforms. However, the current conventional approaches fail in providing promising results. Thus, in this paper, we propose novel convolutional attention-based bidirectional modified LSTM by combining the techniques of the next word, next sequence, and pattern prediction with ACD. The proposed approach extracts significant features from public reviews to detect entity and attribute pair, which are treated as a sequence or pattern from a given opinion. Next, we trained our word vectors with the proposed model to strengthen the ACD process. Empirically, we compare the approach with the state-of-the-art ACD models that use SemEval-2015, SemEval-2016, and SentiHood datasets. Results show that the proposed approach effectively achieves 78.96% F1-Score on SemEval-2015, 79.10% F1-Score on SemEval-2016, and 79.03% F1-Score on SentiHood which is higher than the existing approaches.

中文翻译:

餐饮业社交媒体评论的新类别检测

社交媒体平台使用户能够分享他们对不同主题的想法、想法和意见,同时产生大量信息,可用于了解人们对某些产品的情绪。此信息可以有效地应用于方面类别检测 (ACD)。同样,人们的情绪和基于推荐的人工智能 (AI) 驱动的系统也有帮助供应商和其他客户提高标准的趋势。这些系统具有在多个平台上可用的各种业务的应用程序。然而,目前的传统方法未能提供有希望的结果。因此,在本文中,我们通过将下一个词、下一个序列和模式预测技术与 ACD 相结合,提出了新颖的基于卷积注意力的双向修改 LSTM。所提出的方法从公共评论中提取重要特征来检测实体和属性对,它们被视为来自给定意见的序列或模式。接下来,我们使用建议的模型训练我们的词向量以加强 ACD 过程。根据经验,我们将该方法与使用 SemEval-2015、SemEval-2016 和 SentiHood 数据集的最先进 ACD 模型进行比较。结果表明,所提出的方法在 SemEval-2015 上有效实现了 78.96% F1-Score,在 SemEval-2016 上实现了 79.10% F1-Score,在 SentiHood 上实现了 79.03% F1-Score,高于现有方法。我们使用建议的模型训练我们的词向量以加强 ACD 过程。根据经验,我们将该方法与使用 SemEval-2015、SemEval-2016 和 SentiHood 数据集的最先进 ACD 模型进行比较。结果表明,所提出的方法在 SemEval-2015 上有效地达到了 78.96% F1-Score,在 SemEval-2016 上达到了 79.10% F1-Score,在 SentiHood 上达到了 79.03% F1-Score,高于现有方法。我们使用建议的模型训练我们的词向量以加强 ACD 过程。根据经验,我们将该方法与使用 SemEval-2015、SemEval-2016 和 SentiHood 数据集的最先进 ACD 模型进行比较。结果表明,所提出的方法在 SemEval-2015 上有效实现了 78.96% F1-Score,在 SemEval-2016 上实现了 79.10% F1-Score,在 SentiHood 上实现了 79.03% F1-Score,高于现有方法。
更新日期:2020-10-24
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