Research On Text Emoji Analysis Method Based On Semantic Scene Analysis

Twitter,as one of the most popular online social media in the world,is mainly expressed in unstructured short texts,which makes twitter an important data source and research object in the field of natural language processing.People use tweets to exchange knowledge,deliver opinions,and express emotions.Research on sentiments in tweets has positive significance in many fields such as content recommendation,product evaluation,and public opinion supervision.With the development of twitter platform technology,emojis are used more and more frequently,and it is difficult for traditional plain text sentiment analysis technology to perceive the sentiment tendency of complex tweets.The paper explores the fine-grained model of tweet sentiment analysis by studying the text of tweets and emojis in the Twitter scene,analyzes the sentiment polarity of emojis at different knowledge granularities,and finds the degree of influence of emojis on the sentiment of tweets.The main work of this thesis is as follows:(1)In order to make better use of emojis in text sentiment analysis,this thesis proposes a new text sentiment analysis model based on BERT-Bi LSTM-Emoji Att,which uses BERT embedding to replace the traditional word embedding,combined with the Bi LSTM model,and a two-layer attention mechanism Emoji Att including a word-level and a clause-level attention mechanism for emojis.This model has achieved excellent results surpassing the other four baseline models on the three tweet datasets.At the same time,it is confirmed through analysis and reasoning that emojis are important for sentiment analysis of tweets.(2)In order to explore the factors that affect the sentiments of emojis in tweets,this thesis also selected 77 types of emojis belonging to "Smileys" and 3 kinds of Twitter user attributes to construct an ETUP corpus consisting of tweets containing these emojis and the personal homepage information of tweet senders containing these attributes.Then this thesis proposes a fine-grained sentiment clustering model based on Sent2 Emoji and K-Means,which inherits the sentiment analysis model.It is proved on the ETUP corpus that the three proposed attributes have varying degrees of influence on the sentiments of emojis in tweets,and there may be other undiscovered influence factors.(3)In order to combine the research content of this thesis with actual application scenarios,this thesis designs and implements a tweet sentiment analysis system based on a Web framework,and introduces the system requirements,architecture,and core modules of tweet acquisition and tweet sentiment analysis in detail.The system interface was displayed by simulating user operations.