Visualization of movie recommendation system using the sentimental vocabulary distribution map

Abstract

This paper suggests a method to refine a massive collective intelligence data, and visualize with multilevel sentiment network, in order to understand information in an intuitive and semantic way. For this study, we first calculated a frequency of sentiment words from each movie review. Second, we designed a Heatmap visualization to effectively discover the main emotions on each online movie review. Third, we formed a Sentiment-Movie Network combining the MDS Map and Social Network in order to fix the movie network topology, while creating a network graph to enable the clustering of similar nodes. Finally, we evaluated our progress to verify if it is actually helpful to improve user cognition for multilevel analysis experience compared to the existing network system, thus concluded that our method provides improved user experience in terms of cognition, being appropriate as an alternative method for semantic understanding.

Opinion Mining-Based Term Extraction Sentiment Classification Modeling

“…Reproducibility is a ratio of the actual values to the existing sentiment classification results, while precision refers to the accuracy of the prediction. Equations ( 11), (12), and (13) define accuracy, reproducibility, and precision, respectively.…”

“…Nouns tend to reflect properties and sentiments related to products, while adjectives and verbs tend to include information on the subjective opinions and evaluation of users. In addition, adverbs are applied as various types of expression methods and modification functions in the process of document classification [11,12].…”