Sentence And Interactive Text Sentiment Analysis Based On Neural Network

With the rapid spread of the Internet,research on social information has become the focus of information processing.Sentiment analysis can be used in almost every field.Our common sentiment analysis texts are personal statement texts that express personal opinions,but texts generated in social media are interactive texts.This article not only proposes a sentiment analysis method for traditional statement texts,but also proposes constructing an interactive text dataset based on dialogues and makes relevant empirical experiments.The main research content of this paper is as follows:(1)In the traditional statement text sentiment analysis,we mainly study sentiment analysis of sentence-level texts,and propose that the high-order pure dependency association is put into the construction process of the convolutional neural network.The PPD patterns and text are respectively used as two channels of a convolutional neural network.Thus the information with strong discriminative in the text is more emphasized,and we can get a better sentence representation,which contributes to the improvement of the final classification performance.(2)In interactive dialogue scenario,existing sentiment analysis approaches are insufficient in modeling the interactions among people.However,new approaches are critically limited by the lack of labeled interactive sentiment datasets.Thus we present a new conversational dataset for interactive sentiment analysis.We manually label 2,214 multi-turn English conversations collected from various websites.Compared with existing sentiment datasets,the dataset(a)is no longer limited to one specific domain but covers a wide range of topics and scenarios;(b)describes the interactions between two speakers;and(c)reflects the sentimental evolution of each speaker among conversation.Finally,we evaluate various state-of-the-art sentiment analysis algorithms on the dataset,demonstrating the need of interactive sentiment analysis models and the potential of the dataset to facilitate the development of such models.