The invention relates to the technical field of text
language analysis, in particular to a method for evaluating emotion in
client evaluation text content, which comprises the following steps of: S1, establishing a basic emotion
database through the
client evaluation text content, performing similarity screening, invalid deletion and coding on data, and removing texts without evaluation value to obtain a basic emotion
database; carrying out
natural language preprocessing on the residual evaluation texts; s2, performing sentiment classification on the evaluation of the customer based on a
deep learning model, tracking the change of the evaluation of the customer through
time sequence analysis, judging the change of the customer on products and services at different time points by using clustering analysis, and constructing a personalized portrait of the customer, in the method, the change of the evaluation of the customer is analyzed and tracked through A I, and a theme
label is distributed for an evaluation text; and meanwhile, when the BERT
algorithm model is trained, the
training effect of the model is enhanced through MLM
mask, NSP prediction and customer personalized portrait, and the purpose of truly extracting
semantics and emotions in the text is achieved.