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Looking for a Python Module that finds Tags for a Text describing its Content

asked 2023-04-05 09:09:04 +0000

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answered 2023-04-05 09:09:13 +0000

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updated 2023-04-08 11:09:49 +0000

One popular Python module for finding tags or keywords for a given text is the Natural Language Toolkit (NLTK).

To get started, you will first need to install the NLTK module in Python. You can do this using the following command in your terminal or command prompt:

pip install nltk

Once you have installed NLTK, you can use its built-in functionality to tokenize the text into words, remove stop words (common words like "the" and "and" that are unlikely to be good tags), and extract the most frequent words as potential tags.

Here is some sample code to accomplish this:

from nltk.corpus import stopwords
from nltk.tokenize import word_tokenize

def get_tags(text, num_tags=5):
    # Tokenize the text into words
    words = word_tokenize(text.lower())

    # Remove stop words
    stop_words = set(stopwords.words('english'))
    words = [word for word in words if word not in stop_words]

    # Get the most common words
    freq_dist = nltk.FreqDist(words)
    tags = [word for word, _ in freq_dist.most_common(num_tags)]

    return tags

In this example, the gettags function takes in a text parameter containing the input text and an optional numtags parameter to specify how many tags to extract (default is 5).

The function first tokenizes the text into words using the word_tokenize function from NLTK. It then removes stop words using the stopwords module from NLTK.

Finally, the function uses the FreqDist class from NLTK to create a frequency distribution of the remaining words and extracts the num_tags most common words as the final tags.

You can call this function with your input text and get a list of tags that describe the content of the text. For example:

text = "This is a sample text. It is meant to be used for testing purposes."
tags = get_tags(text)
print(tags)  # Output: ['sample', 'text', 'used', 'testing', 'purposes']

Note that this is just a basic example, and there are many other ways to extract tags or keywords from text depending on your specific requirements.

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Asked: 2023-04-05 09:09:04 +0000

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Last updated: Apr 08