Abstract
Sentiment analysis is one of the key tasks of natural language understanding. Sentiment Evolution models the dynamics of sentiment orientation over time. It can help people have a more profound and deep understanding of opinion and sentiment implied in user generated content. Existing work mainly focuses on sentiment classification, while the analysis of how the sentiment orientation of a topic has been influenced by other topics or the dynamic interaction of topics from the aspect of sentiment has been ignored. In this paper, we propose to construct a Gaussian Process Dynamic Bayesian Network to model the dynamics and interactions of the sentiment of topics on social media such as Twitter. We use Dynamic Bayesian Networks to model time series of the sentiment of related topics and learn relationships between them. The network model itself applies Gaussian Process Regression to model the sentiment at a given time point based on related topics at previous time. We conducted experiments on a real world dataset that was crawled from Twitter with 9.72 million tweets. The experiment demonstrates a case study of analysing the sentiment dynamics of topics related to the event Brexit.
Highlights
Opinions are central to almost all human activities
We discuss the construction of the Dynamic Bayesian Network based on the input sentiment time series and the sentiment analysis results of the constructed Dynamic Bayesian Network for sentiment evolution
We have developed a methodology for the analysis of sentiment evolution across multiple related topics that can build dynamic models of the changes in sentiment over time
Summary
Opinions are central to almost all human activities. Sentiment analysis is the field of study that analyses people’s opinions and sentiments. People’s overall sentiment tendency on the topic ‘‘#Brexit’’ on Twitter will be an important indicator of the outcome of political events. Another example is that the topic level sentiment origination of a new product ‘‘#iphone10’’ can be used for the prediction of sales of this new phone model [4], and even the stock price of the relevant company [8]. We propose to apply DBNs to the sentiment evolution domain and use them to model the dynamics and interactions of the sentiment of topics on social media.
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