Text-Preprocessing

Text preprocessing is the initial and crucial step in text analysis, aiming to clean and prepare raw text for further analysis. The common steps involved are:

1. Tokenization:

   • Breaking down text into individual units, such as words or phrases, known as tokens.
   • Example: The sentence "Data science is fascinating!" is tokenized into ["Data", "science", "is", "fascinating", "!"].

2. Stop Words Removal:

   • Removing common words that usually do not contribute significant meaning, such as "and", "the", "is".
   • Helps in reducing the dimensionality of the text data.

3. Stemming:

   • Reducing words to their root form.
   • Example: "running", "runner", and "ran" become "run".

4. Lemmatization:

   • Reducing words to their base or dictionary form, considering the context.
   • Example: "better" becomes "good".

5. Lowercasing:

   • Converting all characters in the text to lowercase to ensure uniformity.
   • Example: "Data Science" becomes "data science".

6. Removing Punctuation and Special Characters:

   • Eliminating punctuation marks and other non-alphanumeric characters.
   • Example: "Hello, World!" becomes "Hello World".

7. Text Normalization:

   • Converting text to a standard format, such as expanding contractions ("can't" to "cannot").