论文标题
食品审查和建议的机器学习
Machine Learning for Food Review and Recommendation
论文作者
论文摘要
食品评论和建议对于在线食品服务网站一直很重要。但是，审查和推荐食物并不简单，因为它可能会被不同的环境和含义所淹没。在本文中，我们使用不同的深度学习方法来解决情感分析，自动审查标签生成和食品评论的检索的问题。我们建议在Nanyang Technological University（NTU）NTU Food Hunter开发基于网络的食品审查系统，该系统结合了不同的深度学习方法，可帮助用户选择食物。首先，我们将BERT和LSTM深度学习模型实施到系统中，以进行食物评论的情感分析。然后，我们开发了一种语音（POS）算法，以自动识别和提取基于POS标签和依赖性解析的评论标签的审核内容中的形容词 - 单词对。最后，我们还培训了一个轮流模型，以重新排序检索结果，以提高基于SOLR的食品评论搜索系统的准确性。实验结果表明，我们提出的深度学习方法对于现实世界中问题的应用有希望。
Food reviews and recommendations have always been important for online food service websites. However, reviewing and recommending food is not simple as it is likely to be overwhelmed by disparate contexts and meanings. In this paper, we use different deep learning approaches to address the problems of sentiment analysis, automatic review tag generation, and retrieval of food reviews. We propose to develop a web-based food review system at Nanyang Technological University (NTU) named NTU Food Hunter, which incorporates different deep learning approaches that help users with food selection. First, we implement the BERT and LSTM deep learning models into the system for sentiment analysis of food reviews. Then, we develop a Part-of-Speech (POS) algorithm to automatically identify and extract adjective-noun pairs from the review content for review tag generation based on POS tagging and dependency parsing. Finally, we also train a RankNet model for the re-ranking of the retrieval results to improve the accuracy in our Solr-based food reviews search system. The experimental results show that our proposed deep learning approaches are promising for the applications of real-world problems.
