Formulating the Right Questions: Methodologies for Sentiment Analysis




In my previous post, I introduced the goal of developing an advanced sentiment analysis tool tailored for social media. Now, let's dive deeper into the research questions driving this project and the methodologies I'll use to answer them. Formulating the right questions is crucial, as it sets the direction for the entire research process. Using a mix of quantitative and qualitative methods allows for a more comprehensive understanding and ensures robust outcomes.






The core of any research project lies in its questions. For this sentiment analysis tool, I've identified three main questions that will guide my research:


  • How can sentiment analysis tools more accurately identify and categorise emotions in social media texts?

  • What are the most effective machine learning algorithms and NLP techniques for improving sentiment analysis accuracy, including the detection of nuances including sarcasm and irony?

  • How can the design of this tool be optimised for user accessibility and interpretability, ensuring users can effectively utilise and understand the sentiment analysis outputs?

These questions not only help tremendously in defining the scope of the project but also ensure that I address the most critical aspects of developing an effective sentiment analysis tool.






To tackle these questions, I'll be using a mixed-methods approach, combining both quantitative and qualitative research methodologies. This approach allows for a more nuanced understanding of the problem and ensures that my findings are both comprehensive and actionable (Chandra & Harindran, 2017). 



Quantitative Methods

Model Development and Evaluation:

  • With the help of my trusted gamer rig complete with an NVIDIA RTX 3090 GPU, I plan to develop and train various machine learning models, including Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), particularly Long Short-Term Memory (LSTM) networks. These models are known for their ability to capture complex patterns in text data.

  • Performance Metrics: The models will be evaluated using standard performance metrics like precision, recall, F1-score, and accuracy. These metrics will provide a clear picture of how well the models are performing in terms of correctly identifying sentiments.








Qualitative Methods

User Feedback and Usability Testing:

  • To enhance user accessibility and interpretability, I plan to conduct usability testing with a diverse group of participants including my assigned cohort research group. This feedback will be crucial in refining the tool's interface and functionalities.

  • Surveys and Interviews: I also intend to gather qualitative data through surveys and interviews with users to understand their experiences and needs. This will help in making the tool more user-friendly and effective.







By combining the quantitative insights from model performance metrics with the qualitative feedback from users, I'll hopefully be able to create a tool that is not only technically sound but also meets the practical needs of its users. This mixed-methods approach should ensure that my research is well-rounded and addresses all aspects of the problem.



References

  • Chandra, V., &  Harindran, A. (2017). Research Methodology. O'Reilly Media (Accessed: 10 June 2024)

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