Exhibit 25.17 Text data analysis process to retrieve hashtags, and extract keywords and noun phrases.
Text data analysis involves the following key steps:
- Cleaning the Raw Text: Before diving into analysis, raw textual data requires some cleaning. This involves:
- Removing white spaces and punctuation: Unnecessary characters are stripped to streamline the data.
- Lowercasing: Converting all text to lowercase ensures consistency and avoids misinterpretations due to capitalization variations.
- Additional techniques: Depending on the specific task, further cleaning steps like removing stop words (common words like "the" and "a") might be necessary.
- Tokenization — Breaking Down the Text: Once cleaned, the text is broken down into smaller units called tokens. These tokens can be individual words, phrases, or even hashtags. Tokenization allows for further analysis and manipulation of the textual data.
- Part-of-Speech Tagging — Unmasking the Role of Words: POS tagging assigns a grammatical label (e.g., noun, verb, adjective) to each token. This classification helps us understand the function of each word within the context of the sentence. Popular POS tag sets like Penn Treebank provide a standardized way to categorize words.
- Extracting Meaningful Information: As depicted in Exhibit 25.17, valuable information can be extracted from the text data, the tokens and their POS tags:
- Hashtags: Identifying trending hashtags can reveal topics of interest or popular discussions.
- Keywords: Frequently occurring keywords provide clues about the overall content and thematic focus.
- Noun phrases: Extracting noun phrases allows us to understand the entities and concepts being discussed.
- Visualizing Text Data: Word clouds offer a visually appealing way to represent text data. Words appear in different sizes, with larger words indicating higher frequency within the text. This allows for a quick grasp of the most prominent themes and topics. Word clouds are often used in presentations, reports, and research to showcase key findings in an engaging way.
With its various techniques like tokenization, POS tagging, and word cloud generation, text analytics unlocks the hidden potential within textual data. By applying these methods, we can gain valuable insights from social media conversations, customer feedback, and online content, empowering better decision-making and a deeper understanding of the world around us.