While NLTK's WordNet is a useful resource for obtaining synonyms, it may sometimes return seemingly unrelated or nonsensical words. Modified sentence : The quick dark -brown fob jump o'er the slothful dogĪlthough it may equally give you this: Modified sentence: The flying Brown_University Fox jump-start all_over the faineant chase Your output might look something like this: Original sentence : The quick brown fox jumps over the lazy dog The function returns the augmented text with the specified number of replacements. It uses the NLTK library to get synonyms for each word in the text and replaces them with a randomly chosen synonym. This function takes in a text string and the number of synonym replacements to perform (default is 5). join (words ) # Print the original and modified sentences print ( "Original sentence:", sentence ) print ( "Modified sentence:", new_sentence ) Words = synonyms # Join the modified words back into a sentence name ( ) ) # Replace the word with a random synonym, if available if len (synonyms ) > 0 : word_tokenize (sentence ) # Loop through each word in the sentence for i, word in enumerate (words ) : # Get the synonyms for the word Sentence = "The quick brown fox jumps over the lazy dog" # Tokenize the sentence into words Please note that you need to have the necessary Python libraries installed in your Python environment to run this code. Here's an example of how to implement synonym replacement using the NLTK library in Python (which you can install with pip install nltk): This generates new negative reviews that have a slightly different wording but still convey the same sentiment. For example, you can replace the word "bad" with "poor" or "terrible". One technique is to use synonym replacement where you replace certain words in the negative reviews with their synonyms. To address this issue, you can use data augmentation techniques to generate synthetic negative reviews from the existing ones. However, the dataset is imbalanced with a lot more positive reviews than negative ones. Suppose you have a dataset of text reviews for a product and you want to classify them as positive or negative. Here's an example of how data augmentation can be used in text data: Data augmentation can be applied to various types of data beyond images, audio, and video.
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