Food Review Analysis and Sentiment Prediction using Machine Learning Models

Dhruv Gupta, Ausho Roup, Diksha Gupta, Avinash Ratre
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Abstract

In this era of the digital world, text, messages, comments, numbers and videos have become an essential source of information. The trend of people trading through e-commerce giants like Amazon and Flipkart is proliferating. It’s necessary to have a model or tool that helps retrieve helpful information from the customers’ online reviews quickly that can also help product manufacturers have a better idea of their product. This paper targets the food industry, and a model is proposed that analyzes the customer reviews based on NLP techniques- TF-IDF Vectorizer and Count Vectorizer. Based on these analyses, customer sentiments are predicted using different machine learning classification algorithms like Logistic regression, Dummy classifier and Random forest classifier.
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使用机器学习模型的食物评论分析和情绪预测
在这个数字世界的时代,文本、信息、评论、数字和视频已经成为重要的信息来源。人们通过亚马逊和Flipkart等电子商务巨头进行交易的趋势正在激增。有必要有一个模型或工具来帮助从客户的在线评论中快速检索有用的信息,这也可以帮助产品制造商更好地了解他们的产品。本文以食品行业为研究对象,提出了一种基于自然语言处理技术的顾客评论分析模型——TF-IDF矢量器和计数矢量器。基于这些分析,使用不同的机器学习分类算法(如逻辑回归、虚拟分类器和随机森林分类器)预测客户情绪。
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