Text mining is powerful because it allows us to analyse large sets of text data to generate new information and insights that would be difficult to find through slow, manual reading. By using computational methods, we can quickly process and analyse vast amounts of text to uncover patterns, trends, and relationships.
Surveys: Responses collected from questionnaires filled out by participants.
Interviews: Transcriptions of conversations with individuals or groups.
Transcriptions: Written records of spoken language, such as speeches or meetings.
Primary Resources: Original documents or firsthand accounts, like historical records or personal diaries.
Articles from Your Literature Review: Academic papers and articles that you have reviewed as part of your research.
These sources provide rich, qualitative data that can be analysed to gain insights into specific research questions or topics.
Social Media: Posts, comments, and interactions from platforms like Twitter, Facebook, or Instagram.
News Sites: Articles, reports, and updates from online news outlets.
Websites: Content from various websites, including blogs, forums, and company pages.
Web scraping involves using automated tools to collect large amounts of text data from the internet. This data can be analysed to understand public opinion, track trends, or gather information on specific topics.
