A Research Of User Interest Recognition Method On Internet

With the rise of social network media,more and more people share news and personal life through social media.Social network activities have become an important part of people's life.Through the social network user speech to identify the interests of social network users,we can carry out targeted activities such as commercial advertising,and also can carry out a certain degree of evaluation and prediction of the social situation users care about.The traditional interest extraction method based on long text is no longer applicable due to the characteristics of short text,scattered content,many slang and nonstandard grammar of social network terms.Therefore,this thesis proposes a neural network model combining three kinds of semantics to identify the interest of Twitter users.In order to analyze the user's interest in a fine-grained way,a neural network model based on attention mechanism is proposed to extract and match the tweet elements,and finally to recognize and analyze the user's interest.The main work of this thesis is as follows:(1)Screening and preprocessing of tweets of interest.In order to ensure the effectiveness of interest recognition and analysis experiments and obtain pure interest tweet data set,this thesis uses twitter API to obtain the tweets of officially certified users in the interest field.According to the characteristics of the structure of tweet,the preprocessing of tweet is carried out.In order to ensure the feature richness of tweets,the tweets were selected according to the length of tweets.Finally,we use the similarity calculation method of interest keyword set and tweet to score tweets,and filter the pure interest tweet data set according to the score order.(2)Interest recognition method.In order to reduce the interference of noise words in tweets to the classification of tweets,this thesis uses the semi dominant semantic LLDA model to obtain the probability vector of the interest of the word membership.In order to effectively expand the semantic characteristics of the words in tweets,this thesis proposes the explicit semantic model linking the external knowledge map to recompile the word vector.In order to make full use of these features to improve the classification effect,the implicit semantic model vector,semi explicit semantic model vector and explicit semantic model vector are input into the text convolution neural network in a multi-channel way.The experimental results show that the vector obtained by multi semantic model can improve the classification effect of tweets.Finally,users' tweet classification results are counted as user interest recognition results.(3)Interest analysis method.In this thesis,the user interest fine-grained element extraction method combined with attention mechanism is used to analyze the user interest and extract the interest entity and emotion vocabulary in the user tweet.First,we embed the attention mechanism which integrates the text grammatical features into the cyclic neural network model,and use the model to get the sequence annotation results of each word in the tweet,and extract the interest entities and emotion words.Then,according to the distance between words in the tweet,the interest entities and emotion words are matched.Finally,experiments show that the method improves the extraction effect of interest elements and the robustness of the method.