Social Listening System Using Sentiment Classification for Discovery Support of Hot Topics

摘要

In recent years, data on SNS has been gathered and utilized for various marketing activities such as advertisement publicity activities, product planning, etc., are being implemented in many companies. When collecting data, it is common to set conditions such as collection period, language, sending country, and keywords. However, it is often necessary to confront a huge amount of data. Furthermore, it is usual that the collected data contains a large amount of unnecessary noise. Therefore, appropriate classification / extraction work is required in order to reach useful information and hot topics. But the operation is not always easy for everyone; hence we aimed to make everyone easily reach them. This system focuses on "sentiment" that many users are interested in. First, collect data such as posted sentences, extract sentiment (such as good, bad, praise and criticism) in them, and store in the database together with the original ones. This operation is automatically executed. Then, it is surveyed what kind of sentiment is included in a target topic, or conversely, what topic has relationship with a certain sentiment. This system searches information from the previously explained database, and aggregates and visualizes it. This operation is executed based on user's input. These functions help us to discover hot topics in SNS from various perspectives because the operation is easy for everyone. In this paper, we explain the function, configuration and usage of this developed system.