To avoid the poor effect of spammer detection in traditional methods based on content and user behavior due to the increase of data and spammers' updated strategy

a spammer detection method based on sentiment analysis and quality control in microblog was proposed. In the method

the pre-processed comments in microblog were quantified by sentiment analysis. Then in the process of quality control of microblog commentary

the suspicious interval was detected according to different distribution between spammers and normal users for hotspot in varying time. Then a model for spammer recognition was established by cluster analysis. The experimental results showed that the method used the emotion score to make the emotion classification more accurately for the comment text

and then adjusted the boundary value range and time threshold range to limit the anomaly detection level. When the boundary value range increased

the anomaly detection range of the abnormal comment was increased

the tolerance was reduced and the detection sensitivity was improved. When the time threshold was expanded

the tolerance was improved and the detection sensitivity was reduced. Therefore

the appropriate choice of the boundary value and time threshold can effectively improve the accuracy of anomaly recognition which is similar to the normal commentary behavior.