My Research Project




Social media has become a central hub for public discourse, impacting everything from business strategies to political campaigns (Kaplan & Haenlein, 2010). The vast amount of user-generated content on these platforms offers a goldmine of data that, when analysed, can reveal valuable insights into public sentiment. This is where sentiment analysis comes in, aiming to extract subjective information from text to understand what people are thinking and feeling (Shamsi, Bayari & Salloum, 2021).



My research project is all about developing a smart sentiment analysis tool designed specifically for social media. The tool will combine advanced Natural Language Processing (NLP) techniques with cutting-edge machine learning algorithms to accurately analyse and categorise the emotional tone of social media posts. Traditional sentiment analysis tools often miss the mark when it comes to capturing the nuances of social media language—things like sarcasm, slang, and context-specific expressions. By leveraging the latest in machine learning, I aim to overcome these limitations and improve sentiment detection accuracy.






The main goal of this research is to create a sentiment analysis tool that can identify the overall tone of social media posts and provide easy-to-understand visual feedback. This tool has practical applications for businesses, which can use it to analyse customer feedback and gauge sentiment towards their products or services. This kind of data-driven decision-making can boost customer satisfaction and retention (He, Zha, & Li, 2013). Political analysts can also benefit by monitoring public reactions to policy changes, elections, and other major events in real-time.

The project will deliver a user-friendly interface that supports real-time sentiment analysis and offers customisable visual reports. Key features will include trend analysis, sentiment heat maps, and detailed sentiment breakdowns over time. Users will be able to filter data by various parameters like time periods, keywords, and demographics. The interface will also show example comments that illustrate the overall sentiment of the feed or post.






To achieve these goals, I'll be using a mixed-method approach, combining both quantitative and qualitative research methods. Data will be collected using APIs from social media platforms like X and Reddit to ensure a diverse dataset that captures a wide range of sentiments and contexts. The data will be preprocessed using NLP techniques such as tokenisation, lemmatisation, and part-of-speech tagging to prepare it for machine learning models (Bird, Klein, & Loper, 2009).

The heart of the research will involve developing and training deep learning models, specifically Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs). These models will be implemented using frameworks like TensorFlow and PyTorch. I'll also explore fine-tuning pre-trained models like BERT and GPT-3 to leverage their advanced language understanding capabilities (Devlin et al., 2019).

After lots (and lots) of testing and validation, it'll be time to develop the frontend system. Stay tuned for progress on my journey!



References



Location: Scotland, UK

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