Sentiment Analysis for Cold Start Items

Aspect-based opinion mining techniques try to extract aspects (e.g., zoom, battery life, etc. for a digital camera) and estimate their ratings (as stars) from customer reviews. Current models are usually trained at the item level which is fine when the item has been reviewed extensively. However, in real-life datasets more than 90% of items have less than 10 reviews, so called cold start items. In this presentation, we introduce a novel model based on Latent Dirichlet Allocation to address the cold start problem. Our experiments on three real-life datasets demonstrate the improved effectiveness of the proposed model.

Samaneh Moghaddam
Applied Researcher
Samaneh Moghaddam is part of the “Rapid Response Data Science Engineering” team at eBay that transforms customer service data into actionable information. She is working on topic and sentiment models to identify and summarize customers’ pain points from user feedback. Samaneh Moghaddam holds a PhD in Computer Science with a thesis on aspect-based opinion mining. She has published several papers in the area of sentiment analysis in the top international conferences such as WWW, ACM SIGIR, ACM CIKM, and ACM WSDM. She has also presented tutorials at ACM SIGIR 2012 and WWW 2013 to introduce her research to the community.

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