Tweet Sentiment Analysis: Classifying Emotions on Twitter
Cleaning text in Python refers to the process of preparing raw text data for further analysis or modeling. The goal of text cleaning is to remove or correct errors, inconsistencies, and irrelevant information in the text, so that it can be used effectively in an analysis or model.
There are several common steps involved in text cleaning, these include:
- Removing punctuation: This step involves removing any punctuation marks, such as periods, commas, and exclamation marks, that may be present in the text;
- Removing numbers: This step involves removing any numerical digits that may be present in the text;
- Removing special characters: This step involves removing any special characters, such as "@" and "#", that may be present in the text;
- Removing white spaces: This step involves removing any extra white spaces that may be present in the text;
- Removing stopwords: This step involves removing commonly used words, such as "the", "is", "and", that may be present in the text, stopwords are often considered irrelevant for the analysis;
- Lowercasing the text: This step involves converting all the text to lowercase, this is useful when working with text data, because words that have the same meaning but written in uppercase or lowercase are considered different;
- Tokenization: This step involves breaking the text into smaller units, such as words or sentences, in order to make it easier to analyze;
- Stemming/Lemmatization: This step involves reducing words to their root form, this can be useful when you want to group words that have the same meaning.
- Define a function called
- Apply the
clean_textfunction to the
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