Sentiment Analysis of Restaurant Reviews on Yelp with Incremental Learning

被引：0

作者：

Doan, Tri ^{[1
]}

Kalita, Jugal ^{[1
]}

机构：

[1] Univ Colorado, 1420 Austin Bluffs Pkwy, Colorado Springs, CO 80907 USA

来源：

2016 15TH IEEE INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND APPLICATIONS (ICMLA 2016) | 2016年

关键词：

Incremental Learning; Sentiment Analysis; Ensemble Learning;

D O I：

10.1109/ICMLA.2016.158

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Sentiment analysis of customer reviews has a crucial impact on a business's development strategy. Despite the fact that a repository of reviews evolves over time, sentiment analysis often relies on offline solutions where training data is collected before the model is built. If we want to avoid retraining the entire model from time to time, incremental learning becomes the best alternative solution for this task. In this work, we present a variant of online random forests to perform sentiment analysis on customers' reviews. Our model is able to achieve accuracy similar to offline methods and comparable to other online models.

引用

页码：697 / 700

页数：4

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