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Appears in Collections:Computing Science and Mathematics eTheses
Title: Novel symbolic and machine-learning approaches for text-based and multimodal sentiment analysis
Author(s): Poria, Soujanya
Supervisor(s): Hussain, Amir
Cairns, David
Keywords: Sentiment Analysis
Issue Date: 1-Feb-2017
Publisher: University of Stirling
Citation: Poria, Soujanya, Erik Cambria, Rajiv Bajpai, and Amir Hussain. "A review of affective computing: From unimodal analysis to multimodal fusion." Information Fusion 37 (2017): 98-125.
Poria, Soujanya, Haiyun Peng, Amir Hussain, Newton Howard, and Erik Cambria. "Ensemble application of convolutional neural networks and multiple kernel learning for multimodal sentiment analysis." Neurocomputing (2017).
Poria, Soujanya, Erik Cambria, Newton Howard, Guang-Bin Huang, and Amir Hussain. "Fusing audio, visual and textual clues for sentiment analysis from multimodal content." Neurocomputing 174 (2016): 50-59.
Poria, Soujanya, Erik Cambria, Alexander Gelbukh, Federica Bisio, and Amir Hussain. "Sentiment data flow analysis by means of dynamic linguistic patterns." IEEE Computational Intelligence Magazine 10, no. 4 (2015): 26-36.
Poria, Soujanya, Erik Cambria, Amir Hussain, and Guang-Bin Huang. "Towards an intelligent framework for multimodal affective data analysis." Neural Networks 63 (2015): 104-116.
Poria, Soujanya, Basant Agarwal, Alexander Gelbukh, Amir Hussain, and Newton Howard. "Dependency-based semantic parsing for concept-level text analysis." In International Conference on Intelligent Text Processing and Computational Linguistics, pp. 113-127. Springer Berlin Heidelberg, 2014.
Poria, Soujanya, Alexander Gelbukh, Erik Cambria, Amir Hussain, and Guang-Bin Huang. "EmoSenticSpace: A novel framework for affective common-sense reasoning." Knowledge-Based Systems 69 (2014): 108-123.
Poria, Soujanya, Nir Ofek, Alexander Gelbukh, Amir Hussain, and Lior Rokach. "Dependency tree-based rules for concept-level aspect-based sentiment analysis." In Semantic Web Evaluation Challenge, pp. 41-47. Springer International Publishing, 2014.
Abstract: Emotions and sentiments play a crucial role in our everyday lives. They aid decision-making, learning, communication, and situation awareness in human-centric environments. Over the past two decades, researchers in artificial intelligence have been attempting to endow machines with cognitive capabilities to recognize, infer, interpret and express emotions and sentiments. All such efforts can be attributed to affective computing, an interdisciplinary field spanning computer science, psychology, social sciences and cognitive science. Sentiment analysis and emotion recognition has also become a new trend in social media, avidly helping users understand opinions being expressed on different platforms in the web. In this thesis, we focus on developing novel methods for text-based sentiment analysis. As an application of the developed methods, we employ them to improve multimodal polarity detection and emotion recognition. Specifically, we develop innovative text and visual-based sentiment-analysis engines and use them to improve the performance of multimodal sentiment analysis. We begin by discussing challenges involved in both text-based and multimodal sentiment analysis. Next, we present a number of novel techniques to address these challenges. In particular, in the context of concept-based sentiment analysis, a paradigm gaining increasing interest recently, it is important to identify concepts in text; accordingly, we design a syntaxbased concept-extraction engine. We then exploit the extracted concepts to develop conceptbased affective vector space which we term, EmoSenticSpace. We then use this for deep learning-based sentiment analysis, in combination with our novel linguistic pattern-based affective reasoning method termed sentiment flow. Finally, we integrate all our text-based techniques and combine them with a novel deep learning-based visual feature extractor for multimodal sentiment analysis and emotion recognition. Comparative experimental results using a range of benchmark datasets have demonstrated the effectiveness of the proposed approach.
Type: Thesis or Dissertation

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