Detecting location from given text without using NLP

NLP is one of the ways of detecting location from given text. But there are other methods that can be used to infer the location (country) from a given text. Here are a few examples:

1. Keyword-based approach: This approach involves identifying keywords or phrases in the text that are related to specific countries or regions. For example, if the text contains words like “sushi”, “sake”, and “anime”, it may suggest that the text is related to Japan. This approach can be useful if the text is specific to a certain topic or domain.
2. Language-based approach: This approach involves identifying the language of the text and then using language-specific models or rules to infer the location. For example, if the text is in Spanish, it may suggest that the location is in a Spanish-speaking country. This approach can be useful if the language of the text is a strong indicator of the location.
3. Network-based approach: This approach involves analyzing the network of connections between the author of the text and other users or entities, such as the location of the IP address used to post the text or the social media accounts associated with the author. This approach can be useful if the text is posted online and there is additional metadata available.

Here’s an example code for the keyword-based approach in Python:

keywords = {
    'Japan': ['sushi', 'sake', 'anime', 'samurai'],
    'France': ['wine', 'cheese', 'Eiffel Tower', 'croissant'],
    'Italy': ['pizza', 'pasta', 'Rome', 'Florence']
}

def infer_country(text):
    text = text.lower()
    for country, country_keywords in keywords.items():
        if any(keyword in text for keyword in country_keywords):
            return country
    return None

text = "I had the best sushi in Tokyo last night."
country = infer_country(text)
print(country)

In this example, we define a dictionary of keywords for each country, and then define a function infer_country that searches for those keywords in the text and returns the corresponding country if found. We apply this function to an example text “I had the best sushi in Tokyo last night” and print the inferred country, which in this case should be Japan.

Note that this approach may not be accurate if the text does not contain any relevant keywords or if the keywords are ambiguous. Therefore, it’s important to use this approach as part of a broader analysis and to validate the results with other sources of information.