Embracing emojis in sarcasm detection to enhance sentiment analysis

Abstract

People frequently share their ideas, concerns, and emotions on social networks, making sentiment analysis on social media increasingly important for understanding public opinion and user sentiment. Sentiment analysis provides an effective means of interpreting people's attitudes towards various topics, individuals, or ideas.
This thesis introduces the creation of an Emoji Dictionary (ED) to harness the rich contextual information conveyed by emojis. It acts as a valuable resource for deciphering the emotional nuances embedded in textual content, contributing to a deeper understanding of sentiment. In addition, the research explores the complex domain of sarcasm detection by proposing a novel Sarcasm Detection Approach (SDA). This approach identifies sarcasm by analysing conflicts between textual content and the accompanying emojis.
The thesis addresses key challenges in sentiment analysis by evaluating and comparing emoji dictionaries and sarcasm detection approaches to enhance sentiment classification. Extensive experimentation on diverse datasets rigorously assesses the effectiveness of these methods in improving sentiment analysis accuracy and sarcasm detection performance, particularly in emoji-rich datasets. The findings highlight the crucial role of emojis as contextual cues, underscoring their value in sentiment analysis and sarcasm detection tasks.
The outcomes of this thesis aim to advance sentiment analysis methodologies by offering insights into preprocessing strategies, leveraging the expressive potential of emojis through the Emoji Dictionary (ED), and introducing the Sarcasm Detection Approach (SDA). The research demonstrates that integrating emojis through these tools substantially enhances both sentiment analysis and sarcasm detection. By utilizing these tools, the study not only improves model performance but also opens avenues for further exploration into the nuanced complexities of digital communication.

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Identifiers

ORCID for Malak Abdullah Alsabban: orcid.org/0000-0002-2668-0206

ORCID for Dame Wendy Hall: orcid.org/0000-0003-4327-7811

ORCID for Mark Weal: orcid.org/0000-0001-6251-8786

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