TWITTER DATA EXTRACTION FOR SEMANTIC ANALYSIS
Abstract
Data extraction is the process of collecting data from a specific source and transferring it to a new environment (e.g., on-site, cloud-based, or hybrid). It is also the process of gathering data from various sources and converting it into a format that can be used for further analysis or processing. This process is essential for transforming raw data into structured formats for systems like databases, cloud storage, or analytical tools. Logical data extraction, uses queries, scripts, or predefined templates to extract specific data points from a database or structured file. Advanced techniques such as Natural Language Processing, Optical Character Recognition, and machine learning algorithms are often required to extract useful information from unstructured data. Proper data extraction ensures that you work with high-quality datasets, which is crucial for training accurate Machine Learning models. On the other hand, extracting unstructured data is more complex than in the case of its structured counterpart. No wonder – the types of data that constitute this group are highly varied. In this paper, I will extract the data for semantic analysis through the Natural Language Process. Semantic analysis is an essential sub-task of Natural Language Processing and the driving force behind machine learning tools like chatbots, search engines, and text analysis. I will extract meaningful information from unstructured data, such as emails, support tickets, and customer feedback using a semantic analysis-driven tool. For semantic analysis of Twitter data, the goal is often to extract meaningful insights, identify sentiment, and understand relationships in text. The process typically involves several steps: data extraction, preprocessing, text representation, semantic analysis, and interpretation.
