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Exploratory Data Analysis and Data Mining on Yelp Restaurant Review

机译:Yelp餐厅评论探索性数据分析与数据挖掘

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Exploratory data analysis (EDA) a vital role in the interpretation of the contents behind the data and offers descriptive and inferential analysis for data. The data mining technique helps to identify the topics contained in the text corpus. In the present study, we manipulated the Yelp dataset and have applied visual analysis to determine the significant features and key characteristics of the data. We also analyzed temporal and spatial data for a restaurant as a case study. Moreover, we applied the Term Frequency method (Bag-of-Words) on reviews given by the users to get frequent words and phrases in each review class, whether positive, negative, or neutral. Finally, we were able to identify some relationships between the restaurant and the results of the rating system, as well as extract the most important topics that we expect to have the highest impact on the customer's experience.
机译:探索性数据分析(EDA)在解释数据背后的内容中的重要作用,并为数据提供描述性和推理分析。 数据挖掘技术有助于识别文本语料库中包含的主题。 在本研究中,我们操纵了Yelp数据集并应用了视觉分析以确定数据的重要特征和关键特性。 我们还分析了餐厅的时间和空间数据作为案例研究。 此外,我们在用户提供的评论中应用了术语频率方法(袋词),以获得每次评论类中的频繁单词和短语,无论是正,负还是中性。 最后,我们能够识别餐厅之间的一些关系以及评级系统的结果,以及提取我们希望对客户体验产生最高影响的最重要的主题。

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