论文标题
部分可观测时空混沌系统的无模型预测
Cross-Domain Consumer Review Analysis
论文作者
论文摘要
本文对四个流行评论数据集进行了跨域评论分析：亚马逊，Yelp，Steam，IMDB。分析是使用Hadoop和Spark进行的，该分析允许对大型数据集进行有效且可扩展的处理。通过检查这四个在线论坛中的近1200万次评论，我们希望多年来发现销售和客户情绪的有趣趋势。我们的分析将包括对随着时间的评论及其分布的研究，以及对各种评论属性（例如upvotes，创建时间，评分和情感）之间的关系的检查。通过比较跨不同领域的评论，我们希望深入了解推动客户满意度和参与不同产品类别的因素。
The paper presents a cross-domain review analysis on four popular review datasets: Amazon, Yelp, Steam, IMDb. The analysis is performed using Hadoop and Spark, which allows for efficient and scalable processing of large datasets. By examining close to 12 million reviews from these four online forums, we hope to uncover interesting trends in sales and customer sentiment over the years. Our analysis will include a study of the number of reviews and their distribution over time, as well as an examination of the relationship between various review attributes such as upvotes, creation time, rating, and sentiment. By comparing the reviews across different domains, we hope to gain insight into the factors that drive customer satisfaction and engagement in different product categories.
