Incremental Opinion Mining Using Active Learning over a Stream of Documents

Today, opinion mining is one the most important applications of natural language processing which requires special methods to process documents due to the high volume of comments produced. Since the users’ opinions on social networks and e-commerce websites constitute an evolving stream, the application of traditional non-incremental classification algorithm for opinion mining leads to the degradation of the classification model as time passes.
Moreover, because the users’ comments are massive, it is not possible to label enough comments to build training data for updating the learned model. Another issue in incremental opinion mining is the concept drift that should be supported to handle changing class distributions and evolving vocabulary.
In this paper, a new incremental method for polarity detection is proposed which with the application of stream-based active learning selects the best documents to be labeled by experts and updates the classifier. The proposed method is capable of detecting and handling concept drift using a limited labeled data without storing the documents. We compare our method with the state of the art incremental and non-incremental classification methods using credible datasets and standard evaluation measures. The evaluation results show the effectiveness of the proposed method for polarity detection of opinions.