Smart Recommendation System Based on Product Reviews Using Random Forest

Gayatri Khanvilkar, D. Vora
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引用次数: 2

Abstract

Social network, e-commerce sites, blogs are new emerging platforms for people to express their opinion. These sites contain huge amount of text which can be used for different purpose like Sentiment Analysis. Sentiment Analysis is a growing field in natural language processing. Sentiment analysis is major focused on company's improvement. But sentiment analysis can be useful in recommendation system also. Based on various performance measures, this paper compares the results of machine learning algorithms like Multinomial Naive Bayes algorithm, Logistic Regression, SVM Classifier, Decision Tree and Random Forest. These algorithms are used for sentiment analysis of reviews and in turn for product recommendation. In proposed system, Random Forest shows outstanding performance. To create suitable recommendations using the analysis of emotions, there is a need to use polarity obtained through the reviews.
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基于随机森林产品评论的智能推荐系统
社交网络、电子商务网站、博客都是人们表达意见的新兴平台。这些网站包含大量的文本,可以用于不同的目的,如情感分析。情感分析是自然语言处理中的一个新兴领域。情绪分析主要关注公司的改善。但是情感分析在推荐系统中也很有用。基于各种性能指标,本文比较了多项朴素贝叶斯算法、逻辑回归、支持向量机分类器、决策树和随机森林等机器学习算法的结果。这些算法用于评论的情感分析,进而用于产品推荐。在该系统中,随机森林表现出了优异的性能。为了使用情绪分析创建合适的建议,需要使用通过评论获得的极性。
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