Emotional Tendency Dictionary Construction for College Teaching Evaluation

Abstract

To deal with a large amount of emotional information, the text was analyzed by constructing a sentiment dictionary. The mining system was used in the teaching evaluation of colleges and universities. Taking the high-quality emotional dictionary construction algorithm as the research object, a general emotion dictionary construction method based on function optimization was proposed. This method transformed the universal emotion dictionary construction problem into a function optimization problem and used a simulated annealing algorithm to solve it. A universal sentiment dictionary was constructed using the Modularity optimization method in the discovery of complex web communities. In addition, the traditional Modularity method was improved. The results showed that the improved method only compared the Modularity values for all bipartite cases and optimized them. This not only made Modularity method applicable to this problem, but also greatly reduced the amount of computation. In summary, the traditional information clustering method is improved. By making full use of the relationship between the emotional words and documents in the source domain and the target domain, the domain emotional dictionary in the target domain is established.