The invention discloses a text classification model optimization method based on
crowdsourcing feedback and active learning. The method comprises the following steps that: selecting a text dataset, dividing the text dataset into an initial
training set and a residual dataset; obtaining a word from the text dataset; constructing the
feature set of the text dataset, and carrying out vectorization on the text dataset; and introducing the active learning on a classification model, predicting the sentiment polarity of the text dataset subjected to the vectorization, and combining a
crowdsourcing feedback information optimization model to obtain a text
classification result. By use of the method, constructing is used for collecting manually annotated reasons, more
user information is obtained, the
subjective feeling of people is mined,
crowdsourcing feedback information is fused into the model in a
weight change way, and the text classification model is optimized so as to improve model classification performance. An active learning
algorithm is also introduced, and an
annotation sample with a highest value is picked up and handed to a crowdsourcing platform to be annotated so as to lower
annotation cost. Under a limited budget,
annotation accuracy is improved, and the problem that a text classification task containing
label data is in shortage is solved.