A Study on the Sentiment Analysis of Netizens Response to Subjects This study proposes a sentiment analysis system to explore the response and the preference of netizens on special social network subjects. Our proposed system consists of 4 procedures: (1) crawls a large amount of network articles and messages for data collection; (2) proposes a vocabulary encoding model based on semantic text encoding method (word embedding) for text processing; (3) develops an emotional learning classification model based on word2vec and support vector machine to classify articles into three emotions; (4) implements sentiment analysis based on emotional learning classification model to obtain the preference of the subjects. Our system can effectively classify the messages of different subjects into different emotional categories to observe the netizensu0027 response to the subject, and further understand the subjectu0027s attention and preference. Moreover, our system also focuses on the comparison of preferences between multiple subjects to understand whether a subject is more popular with netizens than other subjects. Then, we can clearly observe and grasp the influence of these subjects.

Personalized Recommendation Model: An Online Comment Sentiment Based Analysis Traditional recommendation algorithms measure usersxe2x80x99 online ratings of goods and services but ignore the information contained in written reviews, resulting in lowered personalized recommendation accuracy. Usersxe2x80x99 reviews express opinions and reflect implicit preferences and emotions towards the features of products or services. This paper proposes a model for the finegrained analysis of emotions expressed in usersxe2x80x99 online written reviews, using film reviews on the Chinese social networking site Douban.com as an example. The model extracts featuresentiment word pairs in user reviews according to four syntactic dependencies, examines film features, and scores the sentiment values of film features according to user preferences. User group personalized recommendations are realized through user clustering and user similarity calculation. Experiments show that the extraction of user featuresentiment word pairs based on four syntactic dependencies can better identify the implicit preferences of users, apply them to recommendations and thereby increase recommendation accuracy.