Construction of Multi-Featured Barrage Data Sentiment Monitoring Model Under the"Internet+"Environment
A multi feature fusion ALBERT-SA-BIGRU model is proposed to address the issue of insufficient consideration of customer emo-tional characteristics in enterprise marketing activities,resulting in unsatisfactory marketing outcomes.Firstly,based on the barrage data of enterprise marketing activities,construct an emoticon dictionary and related corpus.Then,the bullet text and bullet attributes are jointly input into the ALBERT model to extract the feature representation of the bullet text,and fused with the pre trained emoji features of GloVe.Next,us-ing self attention mechanism to capture the relationship between emoticons,bullet text,and bullet attributes,the captured word features are input into BiGRU to capture information in both forward and backward directions,strengthen semantic dependencies,and extract emotional features.Finally,use Softmax logistic regression to classify emotional tendencies and construct an emotional monitoring graph.The perfor-mance verification of the model using 163 253 bullet screen data from a certain internet marketing platform shows that the accuracy,preci-sion,and recall rates of the model are 88.8％,88.7％,and 88.9％,respectively.Compared with other models,the model has improved to a certain extent and can provide support for intelligent and precise marketing of user sentiment monitoring in marketing activities for enterprises.
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