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Sentiment Classification Using Convolutional Neural Networks
Open AccessArticle

An Improved Study of Multilevel Semantic Network Visualization for Analyzing Sentiment Word of Movie Review Data

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Lifemedia Interdisciplinary Program, Ajou University, Suwon 16499, Korea
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Science of Language, MoDyCo UMR 7114 CNRS, University Paris Nanterre, 92000 Nanterre, France
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Department of Digital Media, Ajou University, Suwon 16499, Korea
*
Author to whom correspondence should be addressed.
Appl. Sci. 2019, 9(12), 2419; https://doi.org/10.3390/app9122419
Received: 30 April 2019 / Revised: 31 May 2019 / Accepted: 31 May 2019 / Published: 13 June 2019
(This article belongs to the Special Issue Sentiment Analysis for Social Media)
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. View Full-Text
Keywords: collaborative schemes of sentiment analysis and sentiment systems; review data mining; semantic networks; sentiment word analysis collaborative schemes of sentiment analysis and sentiment systems; review data mining; semantic networks; sentiment word analysis
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Ha, H.; Han, H.; Mun, S.; Bae, S.; Lee, J.; Lee, K. An Improved Study of Multilevel Semantic Network Visualization for Analyzing Sentiment Word of Movie Review Data. Appl. Sci. 2019, 9, 2419.

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