44 results about "Emotional score" patented technology

The invention provides a bullet screen distribution method and device, wherein the method comprises the steps of: S1, grouping users in a current live broadcasting room, and, according to word vectors of bullet screens to be distributed currently sent by the various users and word vectors of first historical bullet screens sent by the various users within the first pre-set historical time slot, obtaining the emotional scores of the bullet screens to be distributed and the first historical bullet screens by using a trained LSTM neural network; S2, according to the emotional scores of the first historical bullet screens, obtaining the total emotional scores of the first historical bullet screens sent by each group of users; and S3, based on judgement on the total emotional scores of the first historical bullet screens sent by each group of users, sending the bullet screens to be distributed to each group of users correspondingly according to the emotional scores of the bullet screens to be distributed. According to the bullet screen distribution method and device provided by the invention, the bullet screens are distributed according to the total emotional scores of the first historical bullet screens sent by each group of users within the first pre-set historical time slot and the emotional scores of the bullet screens to be distributed currently sent by the users; therefore, the bullet screens are directionally distributed to each group of users according to the emotional scores of the bullet screens; and the interaction participation enthusiasm of the users is improved.

The invention relates to a short text emotional value calculation method based on sentence structures and contexts. The method comprises: obtaining any to-be-analyzed text data; performing word segmentation processing on the text data through a Java-based word segmentation program; combined with a string matching algorithm, determining sentence structures of the to-be-analyzed text by computer programming; and making the to-be-analyzed text corresponding to the corresponding sentence structure, calculating an emotional score of each clause; implementing a contextual emotional score of the to-be-analyzed text. Part of the emotional score is an emotional value from comment data appearing before the text, influence on current data is calculated according to the distance from the current data,and the other part is an emotional value produced by the news. Establishment of an emotional dictionary is based on an open-source emotional dictionary, that is, emotional vocabulary noumenon of Dalian University of Technology. The emotional value of the short text is calculated combined with the emotional dictionary. Accuracy of the emotional values calculated by the method is higher, and effectin the field of public opinion analysis will be better.

The invention provides a contacts sorting method and device. The method can comprise the following steps: acquiring emotional scores of various contacts in an address book; and sorting the contacts inthe address book according to the emotional scores of various contact. The embodiment of the invention can quickly find the contacts needed to be searched.

The invention discloses a social text sentiment classification method and system based on a text graph neural network, and belongs to the technical field of natural language processing. Comprising the following steps: receiving a target text, and removing an abnormal value in the received text; obtaining word embedding of the target text by utilizing a BERT model; obtaining emotional polarity characteristics of the target text, calculating an emotional score of each word of the target text by using a SentiWordnet emotional dictionary source, and taking a final score of each word as the emotional polarity characteristics of the word; splicing the word embedding and sentiment polarity features of the target text to form an initial word vector; and constructing the target text into a text graph structure, taking the initial word vector as a node initial feature of a text graph, then performing feature extraction by using a text graph neural network message passing mechanism, and finally performing sentiment classification on the extracted feature. The method not only considers the context features in the speech, but also considers the mutual relation between the speech, so that the sentiment classification is more accurate.