Improving annotation categorization performance through integrated social annotation computation
People can identify and organize their ideas and comments with respect to relevant concept topics through using annotation systems. Those annotation systems are obvious that only supports a simple and manual categorization approach. The manual approach is a difficult and time-consuming task for general annotators. Therefore, we propose a requirement annotation categorization which helps annotators to promote the manual annotation categorization effectiveness. Moreover, we propose an integrated social annotation computation which improves the performance of our annotation categorization. In summary, the proposed annotation categorization is verified through experiments using real users’ data sets. We achieved the 83.11% average accuracy for the proposed annotation categorization with integrated social annotation computation. We also show that the proposed annotation categorization requires only 17–20% average processing time (in comparison with the manual approach) is efficient.