33 results about "Co-occurrence networks" patented technology

The invention relates to the technical field of social networks, in particular to a micro-blog hot word and hot topic mining system and method. The method includes the following steps that content data released in a micro-blog are preprocessed to acquire a candidate hot word sequence; according to the frequency of occurrence and suddenness of candidate hot words in a candidate hot word set at the current moment and in a given historical time window, the vitality of each candidate hot word is worked out, and a hot word set is formed by screening the candidate hot words; according to the hot word set formed by screening the candidate hot words, hot word correlation is worked out, and a hot word co-occurrence network is constructed; according to the hot word co-occurrence network, the hot word set is partitioned through the hot word clustering algorithm based on multi-label propagation to acquire a hot topic set. By means of the micro-blog hot word and hot topic mining system and method, efficient micro-blog hot word and hot topic mining is achieved, and mining precision and processing efficiency are improved.

The invention provides a fine-granularity sentiment classification method based on a sentimental word random co-occurrence network. The method comprises the steps of: forming a random network model based on a word sequence and constructed with sentiment characteristics, namely a sentimental word co-occurrence network model, by use of a random network theory and a word co-occurrence phenomenon through annotation of a sentimental noumenon vocabulary library; and carrying out model reduction on the basis, combining a sentimental word longest match (SWLM) method with a TC (Text Category) algorithm to carry out SWLM-TC unsupervised learning classification, or further combining the sentimental word longest match method with an HMM (Hidden Markov Model) machine learning algorithm to establish a fine-granularity sentiment classification model, and realizing classification prediction by use of the model. According to the method, the fine-granularity sentiment classification of a paragraph-level text can be realized, the precision of a pure TC algorithm is improved so that the classification is accurate; and after an HMM model training is carried out on a sample set by use of the SWLM-TC algorithm, the sentiment classification is carried out on a to-be-tested sample database, the automation of a pure machine learning algorithm is improved.