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Appears in Collections:Computing Science and Mathematics eTheses
Title: Novel symbolic and sub-symbolic approaches for text based and multimodal sentiment analysis
Author(s): Dashtipour, Kia
Supervisor(s): Li, Jingpeng
Keywords: Sentiment analysis
Issue Date: 20-Aug-2019
Publisher: University of Stirling
Abstract: In the era of digital media, e-commerce and social networks, websites allow users to share opinions and feedback about products and services. Customers can make informed decisions by reading the experiences of other users. In addition, customer feedback can be used by the organizations to further improve the offered services. However, the quintillion bytes of data generated per day in different languages such as Persian consisting of user feedback cannot be manually read and analyzed by an individual or an organization, for gauging public opinion. Sentiment analysis is an automated process of computationally understanding and classifying subjective information in multi disciplinary fields such as products, movies, news, public opinion etc. In this thesis, we focus on developing novel methods for Persian text based sentiment analysis. We exploit the developed text-based methods to improve multimodal polarity detection. Specifically, we develop a novel hybrid framework that integrates dependency-based rules and deep neural networks for detecting polarity in Persian natural language sentences. In addition, we develop a Persian multimodal sentiment analysis framework that integrates audio, visual and textual cues to computationally understand and harvest sentiments from videos posted on social media platforms such as YouTube and Facebook.Specifically,a first of its kind,multimodal Persian sentiment analysis dataset is developed, which is then used evaluate the proposed multimodal framework that exploits the hybrid dependency-based sentiment analysis framework and deep neural network based multimodal fusion. Extensive experimental results have proven the effectiveness of the proposed approaches as compared to state-of-the-art approaches including deep neural networks.
Type: Thesis or Dissertation

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