Research on parallel affective classification algorithm

Research on parallel affective classification algorithm

Yu Yonghong to June Shanglin

The extension of traditional single-machine affective classification algorithm is a bottleneck in the implementation of affective classification tasks in mass datasets. On the cloud computing platform Hadoop, the mapreduce of algorithms such as feature extraction, feature vector weighting and emotion classification are realized in the Affective classification task. On the affective Corpus dataset, the precision of affective classification algorithm and the time cost of each algorithm are compared and analyzed. The experimental results verify the validity of the parallel Affective classification algorithm and provide valuable reference information for the user to choose the appropriate algorithm to realize the affective classification task.

Research on parallel affective classification algorithm