Social media enable companies to assess consumers’ opinions, complaints and needs. The systematic and data-driven analysis of social media to generate business value is summarized under the term Social Media Analytics which includes statistical, network-based and language-based approaches. We focus on textual data and investigate which conversation topics arise during the time of a new product introduction on Twitter and how the overall sentiment is during and after the event. The analysis via Natural Language Processing tools is conducted in two languages and four different countries, such that cultural differences in the tonality and customer needs can be identified for the product. Different methods of sentiment analysis and topic modeling are compared to identify the usability in social media and in the respective languages English and German. Furthermore, we illustrate the importance of preprocessing steps when applying these methods and identify relevant product insights.

社交媒体使公司能够评估消费者的意见、投诉和需求。社交媒体分析以产生业务价值的系统和数据驱动分析在术语“社交媒体分析”下进行了总结，其中包括统计、基于网络和基于语言的方法。我们专注于文本数据并调查在 Twitter 上推出新产品期间出现的对话主题以及事件期间和之后的整体情绪如何。通过自然语言处理工具进行的分析以两种语言和四个不同的国家/地区进行，以便可以识别产品在音调和客户需求方面的文化差异。比较不同的情感分析和主题建模方法，以确定社交媒体以及英语和德语各自语言的可用性。