Data Processing and Sampling

Once the data source has been identified, several preprocessing and sampling steps are required to ensure the quality and representativeness of the dataset. First, texts should be carefully selected to reflect the diversity of the target population and minimize potential biases. For tasks such as hate speech and emotion analysis, keyword-based filtering can be useful for identifying relevant content. Data cleaning involves removing irrelevant elements such as HTML tags, URLs, special characters, and excessive whitespace using text-processing tools. Applying language identification and de-duplication helps eliminate non-target language and repeated content. The overall dataset size should be determined based on factors such as research objectives, available resources, annotation budget, human capacity, and project timelines.