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Twitter user profiling based on text and community mining for market analysis

机译：基于文本和社区挖掘的Twitter用户配置文件，用于市场分析

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This paper proposes demographic estimation algorithms for profiling Twitter users, based on their tweets and community relationships. Many people post their opinions via social media services such as Twitter. This huge volume of opinions, expressed in real time, has great appeal as a novel marketing application. When automatically extracting these opinions, it is desirable to be able to discriminate discrimination based on user demographics, because the ratio of positive and negative opinions differs depending on demographics such as age, gender, and residence area, all of which are essential for market analysis. In this paper, we propose a hybrid text-based and community-based method for the demographic estimation of Twitter users, where these demographics are estimated by tracking the tweet history and clustering of followers/followees. Our experimental results from 100,000 Twitter users show that the proposed hybrid method improves the accuracy of the text-based method. The proposed method is applicable to various user demographics and is suitable even for users who only tweet infrequently.
机译：本文提出了基于Twitter用户的推文和社区关系来分析Twitter用户的人口统计估计算法。许多人通过社交媒体服务（例如Twitter）发布他们的意见。实时表达的大量意见作为一种新颖的营销应用程序具有巨大的吸引力。当自动提取这些意见时，希望能够根据用户人口统计学来区分歧视，因为肯定和否定意见的比例因年龄，性别和居住地区等人口统计学而异，所有这些对于市场分析都是必不可少的。在本文中，我们提出了一种基于文本和社区的混合方法来估算Twitter用户的人口，其中通过跟踪推文历史记录和关注者/追随者的聚类来估计这些人口。我们从100,000个Twitter用户获得的实验结果表明，提出的混合方法提高了基于文本的方法的准确性。所提出的方法适用于各种用户人口统计，并且甚至适用于不经常鸣叫的用户。

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