Ternary Sentiment Classification of Airline Passengers’ Twitter Text Based on BERT

Abstract

Traditional word vector generation model cannot solve the polysemy problem of word representation in Twitter text, so, a method is presented here, firstly, by using Bidirectional Encoder Representations from Transformers (BERT), the semantic feature vector of the text can be obtained, and then, the feature vector is inputted into the Softmax classifier to implement the sentiment classification of Twitter text. The experimental datasets are source from passengers’ Twitter comments of USA six airlines, the sentiment classification model based on Embeddings from Language Model (ELMo) as the experimental control group. The experimental results indicate that the proposed model is advantage over the experimental control group by using F1-score as the evaluation index.