This repository contains a versatile code file for Natural Language Processing (NLP) tasks. The code covers a wide spectrum of preprocessing and analysis steps, facilitating effective text mining and feature engineering. Whether you're working on sentiment analysis, text classification, or any NLP project, this code provides a robust foundation.
- Lowercase Conversion: Ensure consistent case for all text data.
- HTML Tag Removal: Strip out HTML tags to extract clean text.
- URL Removal: Eliminate website URLs to enhance text clarity.
- Punctuation Removal: Removing text of punctuation marks to streamline analysis.
- Chat Slangs Treatment: Normalize chat slangs for standardized text processing.
- Spell Correction: Correct spelling errors to improve text quality.
- Stopword Removal: Exclude common stopwords(am, an the, etc.) to focus on meaningful content.
- Emoji Removal: Eliminate emojis to simplify text for analysis.
- Emoji-to-Text Conversion: Translate emojis into textual representation for meaningful analysis..
- Word Tokenization: Segment text into individual words.
- Sentence Tokenization: Break text into sentences for deeper analysis.
- Stemming: Reduce words to their root form for linguistic consistency.
- Lemmatization: Transform words to their base or dictionary form.
- Vectorization: Utilize one-hot encoding, Bag of words and TF-IDF for feature representation.
- WordCloud Visualization: Generate WordCloud visualizations for insightful data exploration.
Contributions are welcome! Feel free to enhance the codebase by adding more preprocessing techniques or improving existing ones. Please follow the standard guidelines for pull requests.
By incorporating this comprehensive NLP codebase, you can expedite your text data preprocessing and analysis, allowing you to focus on deriving valuable insights from your NLP projects. Happy coding!