挖掘投诉以改进产品:用户评论问题短语提取研究

Proceedings of the Ninth ACM International Conference on Web Search and Data Mining Pub Date : 2016-02-08 DOI:10.1145/2835776.2855080

E. Tutubalina

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引用次数: 1

摘要

快速增长的用户评论的可用性已经成为公司从文本意见中发现客户不满的重要资源。许多意见挖掘的研究侧重于从产品的评论中提取客户的意见，并预测他们的情绪倾向或评级，目的是帮助其他用户决定是否购买产品。然而，最近很少有研究对与业务相关的意见任务进行研究，以提取有关产品质量问题或技术故障的更精确的意见。本研究的重点是用户评论中提到的问题短语的提取。我们探索主要意见挖掘任务，以确定来自评论的给定文本是否包含对问题的提及。我们制定研究问题，提出基于知识的方法和概率模型，对用户的短语进行分类，并从在线评论中提取潜在问题指标、方面和相关情绪。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Mining Complaints to Improve a Product: a Study about Problem Phrase Extraction from User Reviews

The rapidly growing availability of user reviews has become an important resource for companies to detect customer dissatisfaction from textual opinions. Much research in opinion mining focuses on extracting customers' opinions from products' reviews and predicting their sentiment orientation or ratings with the aim of helping other users to make a decision on whether to buy a product. However, there have been few recent studies conducted on business-related opinion tasks to extract more refined opinions about a product's quality problems or technical failures. The focus of this study is the extraction of problem phrases, mentioned in user reviews about products. We explore main opinion mining tasks to determine whether given text from reviews contains a mention of a problem. We formulate research questions and propose knowledge-based methods and probabilistic models to classify users' phrases and extract latent problem indicators, aspects and related sentiments from online reviews.

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Proceedings of the Ninth ACM International Conference on Web Search and Data Mining

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