A social networking system infers a sentiment polarity of a user toward content of a page. The sentiment polarity of the user is inferred based on received information about an interaction between the user and the page (e.g., like, report, etc.), and may be based on analysis of a topic extracted from text on the page. The system infers a positive or negative sentiment polarity of the user toward the content of the page, and that sentiment polarity then may be associated with any second or subsequent interaction from the user related to the page content. The system may identify a set of trusted users with strong sentiment polarities toward the content of a page or topic, and may use the trusted user data as training data for a machine learning model, which can be used to more accurately infer sentiment polarity of users as new data is received.
In one embodiment, a method includes accessing a plurality of communications, each communication being associated with a particular content item and including a text of the communication; calculating, for each of the communications, sentiment-scores corresponding to sentiments, wherein each sentiment-score is based on a degree to which n-grams of the text of the communication match sentiment-words associated with the sentiments; determining, for each of the communications, an overall sentiment for the communication based on the calculated sentiment-scores for the communication; calculating sentiment levels for the particular content item corresponding sentiments, each sentiment level being based on a total number of communications determined to have the overall sentiment of the sentiment level; and generating a sentiments-module including sentiment-representations corresponding to overall sentiments having sentiment levels greater than a threshold sentiment level.
The present invention relates to a sound effect adding method and apparatus, a storage medium, and an electronic device. The method comprises: determining, on the basis of an emotion judgment model, a statement emotion label of each statement of a text to be processed; determining an emotion offset value of said text on the basis of the type of emotion labels which are largest in quantity among the multiple statement emotion labels; for each paragraph of said text, determining an emotion distribution vector of the paragraph according to the statement emotion label of at least one statement corresponding to the paragraph; determining emotion probability distribution of the paragraph on the basis of the emotion offset value and the remotion distribution vector corresponding to the paragraph; determining, according to the emotion probability distribution of the paragraph and sound effect emotion labels of multiple sound effects in a sound effect library, a target sound effect matching the paragraph; and adding the target sound effect to an audio position corresponding to the paragraph in an audio file corresponding to said text. Thus, the effect of automatically selecting and adding sound effects can be implemented, and the efficiency of adding sound effects can be improved.