Abstract: Multi-entity sentiment analysis aims to identify core entities in a text and judge their corresponding sentiment,which is a research hotspot in the field of fine-grained sentiment analysis.However,most existing researches of long text multi-entity sentiment analysis is still in its early stages.This paper proposes a long text multi-entity sentiment analysis model(PAM) based on multi-task joint training.To begin with,the utilization of TF-IDF algorithm for extracting sentences similar to the article title can help eliminate redundant information and reduce the length of text.Subsequently,the adoption of two BiLSTM models for core entity recognition and sentiment analysis tasks respectively enables the acquisition of necessary features.Next,multi-head attention mechanism is employed,which is integrated with relative position information,to transfer the knowledge gained from entity recognition task to sentiment analysis task,thus enabling joint learning of the two tasks.Finally,the proposed Entity_Extract algorithm is used to identify core entities from predicted candidate entities according to the number and position of entities in the text and obtain their corresponding emotions.Experimental results on Sohu news datasets demonstrate the effectiveness of PAM model.

Key words: Long text , Multi-entity, Fine-grained sentiment analysis, Multi-task learning