Multiclassfication Sentiment Analysis Based on the Fused Model The traditional methods of sentiment classification usually see the data has positive and negative two kinds of attitudes only. But actually, the real data has multicategory sentiment, is positive, negative, neutral and not mentioned four classes. Therefore, when using common classifying methods to analyzing the data sentiment, if the number of few class data is too scarce, it is difficult to learn useful information from them and the final classifying result will tend to most classes. In order to obtain accurate classification results, this paper proposes a multiclassification method based on the combination of Bert (Bidirectional Encoder Representation from Transformers) model and Liblinear (A Library for Large Linear Classification) model (It is abbreviated as BLiblinear). Due to the Bert modelu0027s breakthrough in data preprocessing, this paper prepressed training data set, and obtained the word vector and sentence vectors from data. Next, combined with attribute label and sentiment tendency data, the unstructured data was converted into a structured training data set. It was as the standard input data of Liblinear model to construct a classification model. This modelu0027s classification mechanism is one vs. rest, it can effectively solve the heavy class imbalance problem of massive data in multiple classification tasks. In this paper, the classification result of BLiblinear model was compared with several classical multiclassification methods. And the experimental results show that the combination of Bert model and Liblinear of dealing with the text multiclassification problem is more accurate.

The Automatic Text Classification Method Based on BERT and Feature Union For the traditional model based on the deep learning method most used CNN(convolutional neural networks) or RNN(Recurrent neural Network) model and is based on the dynamic characterlevel embedding or wordlevel embedding as input, so there is a problem that the text feature extraction is not comprehensive. In the development environment of the Internet of Things, A method of Automatic text classification based on BERT(Bidirectional Encoder Representations from Transformers) and Feature Fusion was proposed in this paper. Firstly, the texttodynamic characterlevel embedding is transformed by the BERT model, and the BiLSTM(Bidirectional LongShort Term Memory) and CNN output features are combined and merged to make full use of CNN to extract the advantages of local features and to use BiLSTM to have the advantage of memory to link the extracted context features to better represent the text, so as to improve the accuracy of text classification task. A comparative study with stateoftheart approaches manifests the proposed method outperforms the stateoftheart methods in accuracy. It can effectively improve the accuracy of tag prediction for text data with sequence features and obvious local features.