Developed Text Cleaning and Data Processing Functions

📅 2023-11-11 — Session: Developed Text Cleaning and Data Processing Functions

🕒 03:00–04:40
🏷️ Labels: Python, Text Processing, Data Cleaning, Semantic Analysis, Pandas
📂 Project: Dev
⭐ Priority: MEDIUM

Session Goal:

The session aimed to develop and refine Python functions for text cleaning, data processing, and semantic analysis, focusing on error correction, formatting, and data manipulation.

Key Activities:

• Implemented a Python function to merge invalid sections of text into previous valid sections, enhancing data integrity.
• Utilized random.sample for random sampling from lists of tuples, addressing common errors with np.random.choice.
• Outlined a structured approach for semantic analysis, including data preparation and tool selection.
• Developed Python rules for fixing parsing errors using str.replace() and regular expressions.
• Designed functions for text formatting and cleaning, addressing punctuation, spacing, and typographical errors.
• Applied text cleaning functions to merged sections and regenerated lost data sections due to disconnection.
• Converted cleaned tuples into Pandas DataFrames for structured data manipulation.
• Counted word frequencies in Spanish text using NLTK, excluding stopwords.

Achievements:

• Successfully implemented and tested multiple text processing and cleaning functions.
• Enhanced data processing workflows by integrating semantic analysis and data manipulation techniques.
• Converted processed text into structured DataFrames, facilitating further analysis.

Pending Tasks:

• Re-upload original text data to regenerate lost merged sections.
• Further testing and validation of text cleaning functions on larger datasets.