A film review emotion visualization method for box office prediction

Abstract

The invention discloses a movie review emotion visualization method for box office prediction, which collects movie information and movie review data, and preprocesses the collected data; for the movie review data, uses word segmentation vector method to extract movie review emotion type and intensity features; designs movie reviews Emotional visual coding indicates the type and intensity of emotion; design the temporal and spatial distribution of film review emotion; realize the visual layout of film review emotion and box office prediction. The present invention makes up for the lack of correlation analysis of data attributes and the lack of comprehensive analysis of movie review emotion types and strengths in the current situation, and uses intuitive visual elements and flexible interactions to encode and map movie review emotion types and strengths.

Description

technical field [0001] The invention relates to the technical field of information visualization analysis, in particular to a movie review emotion visualization method for box office prediction. Background technique [0002] In recent years, movies have gradually become one of people's main daily entertainment activities. At the same time, movie reviews have also become an important reference for people to choose movies, and movie reviews have gradually become one of the factors that affect movie box office. However, the emotion expressed in film reviews is difficult to be objectively quantified. The existing film review emotion research for box office prediction is difficult for film industry analysts to intuitively explore and analyze film emotion from multiple angles, leading film industry analysts to analyze film review emotion. The impact on box office forecasts is somewhat challenging. [0003] In the past few years, researchers have used different research methods to...

AI Technical Summary

Problems solved by technology

First of all, statistical analysis methods have problems such as single analysis results and shallow analysis; data mining algorithms have the characteristics of poor interpretability of analysis results.

At the same time, the existing research on film review sentiment methods for box office prediction fails to better combine the multiple structural characteristics of this type of data such as time and space, and does not mine the laws contained in the relationship between data features and features.

Secondly, there are relatively few studies using visualization and visual analysis methods. Existing studies have shortcomings such as simple analysis problems, single visual views, and lack of view interaction. They rarely pay attention to the diversity of movie review data attributes and the correlation between attributes. Analysis of the impact of film review data on box office forecasting from multiple perspectives

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