Identification of item features in microblogging data

In recent years, microblogging services have become very popular. The larger volume of real-time information generated by millions of users, more important to extract useful information from the microblogging services will be. In this work, we want to use opinion mining to find the relevant and significant features of items from the microblogging services, like Twitter. We construct a sentiment-based framework to identify the relevant features in microblogging. Our method consists of two stages. First, the data process stage processes the raw data from microblogging services. Then, in second stage we extract the relevant features by the sentiment characteristics from these messages and utilize these extracted features to construct the relevant feature network and group them according their concepts relations. Therefore, our system could be applied for knowing the characteristics of a product quickly and explicitly. In our experiments, our system can identify the popular item features in different domains effectively and the same concept features can cluster together in small groups.