Abstract
This paper suggests a method for refining a massive amount of collective intelligence data and visualizing it with a multilevel sentiment network in order to understand the relevant information in an intuitive and semantic way. This semantic interpretation method minimizes network learning in the system as a fixed network topology only exists as a guideline to help users understand. Furthermore, it does not need to discover every single node to understand the characteristics of each clustering within the network. After extracting and analyzing the sentiment words from the movie review data, we designed a movie network based on the similarities between the words. The network formed in this way will appear as a multilevel sentiment network visualization after the following three steps: (1) design a heatmap visualization to effectively discover the main emotions on each movie review; (2) create a two-dimensional multidimensional scaling (MDS) map of semantic word data to facilitate semantic understanding of network and then fix the movie network topology on the map; (3) create an asterism graphic with emotions to allow users to easily interpret node groups with similar sentiment words. The research also presents a virtual scenario about how our network visualization can be used as a movie recommendation system. We next evaluated our progress to determine whether it would improve user cognition for multilevel analysis experience compared to the existing network system. Results showed that our method provided improved user experience in terms of cognition. Thus, it is appropriate as an alternative method for semantic understanding.
Highlights
We suggested a virtual scenario in thehow general public’s view in order to determine how in the general public’s view in order to determine this network visualization could be applied this network visualization could be applied in the actual movie recommendation process
Wewe will explore whether the network visualization put forth thisresearch researchcan canimprove improveuser usercognition
This study study proposed proposed three three different different methods methods for for intuitive intuitive and and semantic semantic analysis analysis based based on on the the multilevel sentiment network visualization that we created from movie review data to serve as collective multilevel sentiment network visualization that we created from movie review data to serve as intelligence
Summary
We are faced with an enormous amount of information every day due to the vast growth of information and communications technology. There is increased interest in effective data processing and analysis. Big data is playing an increasingly important role since it is suitable for refined and semantic processing, even if the amount of data is considerable or if its structure is complex [1]. Big data has attracted great attention in the field of data visualization primarily for the design of efficient processing and semantic analysis. Data visualization is a redesigned concept of data analysis with better readability, offering distinct insights that cannot be grasped from a table or graph [2]. Network visualization is a visual tool to semantically analyze data if there is a
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