Extraction and Categorization of Tip Information from Social Media

In social media, users post freely many kinds of information which is related to personal behavior, experimentation and their own sentiments on the social media. These information is not written in ordinary web pages, but it is sometimes important information for the users. However, it is difficult to extract such important information from social media, because so much information exists. We propose a method to extract such important information from social media. We call such information “tip information”. Our proposed tip information is including a user’s experiment and it has common important words. We call the common important words “tip keywords”. We first extract user’s experience sentences from social media based on experience mining method. Next, we extract sentences which are include tip keywords from them. They become tip information. After extracting tip information, we categorize it according to four categories which are “suggestion and recommendation”, “restraint”, “briefing”, and “possible and impossible”. Then we present tip information based on each category.