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Related Experiment Videos

A Multilayer Naïve Bayes Model for Analyzing User's Retweeting Sentiment Tendency.

Mengmeng Wang¹, Wanli Zuo¹, Ying Wang²

¹College of Computer Science and Technology, Jilin University, Changchun 130012, China ; Key Laboratory of Symbolic Computation and Knowledge Engineering, Jilin University, Ministry of Education, Changchun 130012, China.

Computational Intelligence and Neuroscience

|September 30, 2015

Summary

This study introduces a new framework for analyzing user sentiment tendencies in microblogging by modeling dynamic retweeting features. The research highlights the importance of temporal information for accurate sentiment analysis in social networks.

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Area of Science:

Social Network Analysis
Natural Language Processing
Computational Social Science

Background:

Microblogging platforms facilitate rapid information diffusion through user retweeting behavior.
Understanding user sentiment in retweets is crucial for analyzing information spread dynamics.
Dynamic social networks, like microblogs, require methods that capture evolving user interactions and content.

Purpose of the Study:

To investigate the exploitation of dynamic retweeting sentiment features for sentiment tendency analysis in microblogging.
To develop a novel framework for analyzing user sentiment in dynamic social networks.
To assess the impact of dynamic features and temporal information on retweeting sentiment analysis.

Main Methods:

Modeling dynamic retweeting sentiment features using time series of network structure and text information.
Building separate Naïve Bayes models based on profile, relationship, and emotion dimensions.
Developing a multilayer Naïve Bayes model integrating multidimensional models for comprehensive sentiment analysis.

Main Results:

The proposed framework effectively analyzes user retweeting sentiment tendency on real-world microblogging data.
Dynamic retweeting sentiment features and temporal information significantly improve analysis accuracy.
The study validates the effectiveness of the multidimensional and multilayer Naïve Bayes approach.

Conclusions:

Dynamic retweeting sentiment features are vital for accurate sentiment tendency analysis in microblogs.
The developed multilayer Naïve Bayes model offers a robust approach for sentiment analysis in dynamic social networks.
This research provides a new perspective for understanding user sentiment in evolving online environments.