Location Inference Models work to infer the location of the author of a piece of text content, by assessment and predicting on a number of parameters in the data.

Example Use Cases

• In conjunction with aggregations and sentiment, high-level assessments of sentiment towards a brand in a specific city could be delivered to a product's dashboard.
• Spanish language location Inference can give a country-level view of Spanish content rather than relying on keywords or language.
• Japanese language location Inference can give a country-level view of Japanese content rather than relying on keywords or language.
• Location Inference can give a more city-level view of content than relying on keywords or language.
• Location Inference can be used in its inverse to remove certain cities or countries from the results of content in a specific area.

Model Categories

We have two categories of Location Inference models, which are based on the conversational patterns present on the source:

• Conversational-Style Models
• Broadcast-Style Models

Within the two categories is a separate model per language.

Conversational-Style Models are trained on social content that has more text features. They require more text features to be present in the data for better prediction, and can predict on a more granular level.

Broadcast-Style Models are trained on social content lacking large amounts of surrounding context. However, they can be more limited in their coverage.

📘

If you are using a Broadcast-style source, and require labels only present in Conversational-style Models, you can still use the Conversational-style Models for the increased coverage, however accuracy may be lower as a result.

Suggested Model Type for Example Sources:

This is only a suggestion and guide, not a comprehensive list of sources.

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Sources written in a formal style, such as news, reports, press releases, e-commerce listings, and other formally written data are best to use other location detection models. This is due to length, language, and de-personalization of writing patterns.

Available Location Inference Models

The following models are available:

Data labels for each model are available below.

Labels

Conversational-Style Models

Broadcast-Style Models

Metadata Output

This location inference classifier outputs three labels: city, region, and country of origin for a given text and an associated confidence score. If the confidence is under 0.5 or not in one of the trained labels or the output is unknown then the "Other " tag is applied.
The label would be one of the city names for the city and ISO 3166-1 code for region and country.

"location_inference": {
                    "label": "Detroit",	 
                    "confidence": 0.5681
                },
"location_inference_region": {
                    "label": "MI",	 
                    "confidence": 0.8361
                },
"location_inference_country": {
                    "label": "US",	 
                    "confidence": 0.8681
                },

Source Specific Information

English Conversational-Style provides 61 cities' inferred locations for integrated data sources and 74 cities inferred locations for post-processing operations. The following cities, regions, and countries are available in English:

• Amsterdam, NL
• Anchorage, AK, US
• Atlanta, GA, US
• Austin, TX, US
• Baltimore, MD, US
• Barcelona, ES
• Berlin, DE
• Boston, MI, US
• Brussels, BE
• Budapest, HU
• Cairo, EG
• Cape Town, ZA
• Charleston, SC, US
• Charlotte, NC, US
• Cheyenne, WY, US
• Chicago, IL, US
• Columbus, OH, US
• Copenhagen, DK
• Dallas, TX, US
• Delhi, IN
• Denver, CO, US
• Des Moines, IA, US
• Detroit, MA, US
• Doha, QA
• Dubai, AE
• Dublin, IE
• El Paso, TX, US
• Fargo, ND, US
• Fort Worth, TX, US
• Houston, TX, US
• Huntsville, AL, US
• Indianapolis, IN, US
• Jacksonville, FL, US
• Johannesburg, ZA
• Kansas City, MO, US
• Las Vegas, NV, US
• Lima, PE
• London, UK
• Los Angeles, CA, US
• Louisville, KY, US
• Madrid, ES
• Manila, PH
• Melbourne, AU
• Memphis, TN, US
• Mexico City, MX
• Milwaukee, WI, US
• Minneapolis, MN, US
• Montreal, QC, CA
• Mumbai, IN
• Naples, IT
• New Orleans, LA, US
• New York, NY, US
• Newark, NJ, US
• Oklahoma City, OK, US
• Paris, FR
• Philadelphia, PA, US
• Phoenix, AZ, US
• Portland, OR, US
• Prague, CZ
• Riyadh, SA
• Sacramento, CA, US
• Salt Lake City, UT, US
• San Francisco, CA, US
• San Diego, CA, US
• Santa Fe, NM
• Seattle, WA, US
• Singapore, SG
• Sydney, AU
• Tokyo, JP
• Toronto, CA
• Virginia Beach, VA, US
• Washington DC, WA, US
• Wichita, KS, US
• Zurich, CH