摘要： 在海量数据集上执行情感分类任务时,传统的单机情感分类算法的扩展性成为系统的瓶颈。在云计算平台Hadoop上,实现了情感分类任务中特征提取、特征向量加权和情感分类等算法的MapReduce化。在情感语料数据集上,对各种子步骤组合下情感分类算法的精度及每种算法的时间开销进行了对比分析。实验结果验证了实现的并行化情感分类算法的有效性,同时它为用户选择合适算法实现情感分类任务提供了有价值的参考信息。
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