Use this URL to cite or link to this record in EThOS: https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.757487
Title: Twitter sentiment analysis in the era of emojis
Author: Li, Mengdi
Awarding Body: University of Nottingham
Current Institution: University of Nottingham
Date of Award: 2018
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Abstract:
Twitter has become an important site for national discussions where we can get a new and timely update of the public opinion towards any event. Twitter Sentiment Analysis (TSA) can be an effective method for unpacking the deep insights embodied within the opinions of the public. Recently, various TSA techniques have been developed, but little consideration has gone into emojis, which is a new invention and has been popularly shared by Twitter users from different countries, with various demographic characteristics, and diverse cultural backgrounds. The ubiquitous adoption of emojis on Twitter provides new opportunities to analyse sentiment expressions in a textual context. Emojis should be included when conducting TSA as the meaning of a Twitter post and its sentiment can be identified with greater clarity and accuracy with emojis. This research aims to develop novel approaches that handle emojis properly and tackle current open issues in TSA. Consisting of four phases, this thesis presents a comprehensive and in-depth research work in the field of Emoji Analytics and TSA. Several studies have been conducted to investigate emoji usage on Twitter and evaluate their effects on TSA. The experimental results demonstrate that emojis has become an essential component of Twitter communication and it is an important area of study complementary to TSA, implying promising future research opportunities for TSA. A novel TSA methodological framework that collects, pre-processes, analyses and maps citizen sentiments from Twitter in helping learn citizens’ moods has been implemented and proved to be effective. The novel framework identifies the best setting for TSA when involving emojis, and proposes an effective emoji training heuristic, which is feasible for both ternary and multi-class classification of tweets. Besides, it innovatively includes the visualisation of user-generated contents in a location-based manner on geographical maps, which provides a much easier-to-understand visual representation of the sentiment. The methodological framework has been proved applicable in real-world scenarios and can be used to support research in other fields. Being the first to consider popularity of emojis on Twitter and include them in performing TSA, this research is considered to be a pioneering work in the field, suggesting a new direction for TSA in the era of emojis.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.757487  DOI: Not available
Keywords: QA Mathematics
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