Novel symbolic and machine-learning approaches for text-based and multimodal sentiment analysis - PhDData

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Novel symbolic and machine-learning approaches for text-based and multimodal sentiment analysis

The thesis was published by Poria, Soujanya, in September 2022, University of Stirling.


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
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

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