Traditional sentiment analysis methods do not perform well when applied to social media data. In this study, we propose an approach to improve sentiment analysis performance in the context of social media. Our approach utilizes three types of additional information that can be collected from social media platforms—personal preference, friend influence, and herding effect—to enrich the input features of a supervised sentiment classification model. We implement the approach on data sets collected from Twitter across two industries (airlines and wireless service providers) and present the performance improvement attained by combining social features with pure text-based features. To further investigate the operational implications of this improvement, we develop a stylized service recovery model for customer relationship management in social media. Our work has implications for automating social media monitoring and, more broadly, for improving customer relationship management in organizations.

中文翻译：

---

将文本线索与社交线索相结合：利用社交特征改进社交媒体中的情感分析

传统的情感分析方法在应用于社交媒体数据时表现不佳。在这项研究中，我们提出了一种在社交媒体背景下提高情感分析性能的方法。我们的方法利用可以从社交媒体平台收集的三种附加信息——个人偏好、朋友影响和羊群效应——丰富监督情感分类模型的输入特征。我们对从 Twitter 收集的两个行业（航空公司和无线服务提供商）的数据集实施了这种方法，并展示了通过将社交功能与纯文本功能相结合所获得的性能改进。为了进一步研究这种改进的运营意义，我们为社交媒体中的客户关系管理开发了一个程式化的服务恢复模型。我们的工作对自动化社交媒体监控以及更广泛地改善组织中的客户关系管理具有重要意义。