2025-04-28 13:26
IndustryPreprocess the Text
#CurrencyPairPrediction
Preprocess the Text Raw text is messy — preprocessing cleans it up:
• Tokenization: Split text into words
• Lowercasing: Normalize case
• Removing Stopwords: Words like “the”, “is”, “and” • Stemming / Lemmatization: Reduce words to base form (e.g., “running” -> “run”)
• Named Entity Recognition (NER): Identify companies, locations, monetary amounts
• Handling special financial entities: e.g., recognizing ticker symbols like “AAPL”, “TSLA”
Example: Original: “Tesla Inc. reported a 20% jump in Q1 revenue!” Preprocessed: [‘tesla’, ‘inc’, ‘report’, ‘20%’, ‘jump’, ‘q1’, ‘revenue’]
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Preprocess the Text
Thailand | 2025-04-28 13:26#CurrencyPairPrediction
Preprocess the Text Raw text is messy — preprocessing cleans it up:
• Tokenization: Split text into words
• Lowercasing: Normalize case
• Removing Stopwords: Words like “the”, “is”, “and” • Stemming / Lemmatization: Reduce words to base form (e.g., “running” -> “run”)
• Named Entity Recognition (NER): Identify companies, locations, monetary amounts
• Handling special financial entities: e.g., recognizing ticker symbols like “AAPL”, “TSLA”
Example: Original: “Tesla Inc. reported a 20% jump in Q1 revenue!” Preprocessed: [‘tesla’, ‘inc’, ‘report’, ‘20%’, ‘jump’, ‘q1’, ‘revenue’]
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