Analysing social media data using sentiment analysis in relation to public order

BALDWIN, James (2021). Analysing social media data using sentiment analysis in relation to public order. Doctoral, Sheffield Hallam University.

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Link to published version:: https://doi.org/10.7190/shu-thesis-00439
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    Abstract

    The research aim is to analyse social media data using sentiment analysis in relation to public order. A sentiment can be expressed in a thought, opinion or attitude that is mainly based on emotion instead of reason. (SA) Sentiment Analysis studies the opinions, sentiments and emotions expressed at sentence or document level. SA extracts text which is identified and classified as opinions or emotions that aim to support a decision-making process through the analysis of text. SA identifies and measures whether the text being analysed is positive, negative or neutral in relation to an entity, such as people, organisation, event, location, or a topic. As the adoption of ubiquitous technology increases and the population on social media continues to grow with the speed of responsiveness of the users expressing their political, economic or religious views on Twitter or Facebook, the posts become valuable sources of public opinion. This can be seen as an important commodity to be used to infer public opinions for social studies or marketing. The research suggests the police have found it difficult to adapt their existing model to the changing nature of public events and handling of acceleration towards technology and social media. The scalability and volume of data has made it increasingly hard for the police to manage, monitor and make use of intelligence emerging from social media to maintain the peace. To address this gap, the investigation will evaluate whether SA can enhance the analysis of social media in the context of public (dis)order events. This may help to improve the police’s decision-making process and reduce complexity to increase public safety. There are specific and generalised ways that SA can support the police, but this research might focus on a specific case. To meet the aim, the research proposes to use a SA model, data mining tools and techniques to analyse the relevant data extracted from social media. The project will use an adapted social media lifecycle as a methodological approach. Past events involving public order and the police will be evaluated to develop relevant methodology and provide appropriate recommendations to the technical community on ways to use SA for future applications of social media. In the project it adopted a hybrid approach which consists of a dictionary, machine learning and gold standard approaches. As result, the machine learning of dictionaries and manual classification results proved to show the strongest output based on precision, recall and F1 measure when compared to the machine learning of tweets and manual classification. The change point analysis helped to identify significant points in the timeline of tweets for the event which correlated to the physical event. However, there were some inaccuracies on the allocated points of change, as deemed insignificant based on news media and low volume of tweets. Future work is required to understand the reasons behind the allocation change points and possible use of alternative methods to help extract further insights that could not be explored in this project. The study makes a series of contributions to knowledge. First, to the creation of a keywords for public order events due to none being publicly available. Second, is to build towards a model to predict what may happen in public order events with the application of dictionary, machine learning and creation of gold standard in the realm of sentiment analysis. Third, the technical contribution to sentiment analysis community to help provide future recommendations to potentially enhance their framework and what areas require further research in the area. Fourth, is the development of social media lifecycle methodology, which has been tested in this project.

    Item Type: Thesis (Doctoral)
    Additional Information: Director of studies: Dr. Jotham Gaudoin
    Research Institute, Centre or Group - Does NOT include content added after October 2018: Sheffield Hallam Doctoral Theses
    Identification Number: https://doi.org/10.7190/shu-thesis-00439
    Depositing User: Colin Knott
    Date Deposited: 09 May 2022 15:48
    Last Modified: 09 May 2022 15:49
    URI: http://shura.shu.ac.uk/id/eprint/30213

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