The Semantic Analysis for Hostel Reviews API allows users to analyze customer reviews of hostels and retrieve information about the key themes and sentiments expressed in the reviews. The API returns a list of keywords and phrases, as well as a sentiment score for each review.
{"general_sentiment":0.714,"categories":{"0":{"name":"location","count":1,"sentiment_score":1},"1":{"name":"hostel","count":3,"sentiment_score":1.667},"2":{"name":"service","count":1,"sentiment_score":-1},"3":{"name":"price","count":2,"sentiment_score":0}},"aspects":{"0":{"name":"close to","count":1,"sentiment_score":1},"1":{"name":"opinion","count":2,"sentiment_score":2},"2":{"name":"check-in","count":1,"sentiment_score":-1},"3":{"name":"common room","count":1,"sentiment_score":1},"4":{"name":"payment","count":2,"sentiment_score":0}},"semantic_analysis":{"0":{"id_semantic_model":179,"name_semantic_model":"close_to_airport","description":"The hostel is close to the airport, short drive, minutes from airport","id_opposite_semantic_model":null,"category":"location","aspect":"close to","feature":"airport","polarity":1,"segment":"We only stayed at this hostel for one night because it was convenient to the airport"},"1":{"id_semantic_model":226,"name_semantic_model":"overall_good_satisfied","description":"The customer was satisfied in general, good overall, nice stay, good experience, good place, enjoyable experience, everything we needed, met my expectations","id_opposite_semantic_model":228,"category":"hostel","aspect":"opinion","feature":"overall good, satisfied","polarity":2,"segment":"We only stayed at this hostel for one night because it was convenient to the airport"},"2":{"id_semantic_model":231,"name_semantic_model":"check_in_slow_bad","description":"Check-in/out was slow, took forever, early check in not permitted, long lines, queue, took over an hour, inconvenient","id_opposite_semantic_model":227,"category":"service","aspect":"check-in","feature":"slow","polarity":-1,"segment":"The check in process took 30 minutes, which frustrated us because there were a lot of people checking in at the same time"},"3":{"id_semantic_model":325,"name_semantic_model":"common_social_room_available","description":"The common area was available, there is common area, big social area","id_opposite_semantic_model":349,"category":"hostel","aspect":"common room","feature":"available","polarity":1,"segment":"The shared bathrooms weren't too crowded and the common room had billiards and ping pong"},"4":{"id_semantic_model":120,"name_semantic_model":"bill_problems","description":"There was a problem with the bill/payment, payment problems, double charged, incorrectly charged, card mix up, bill mess up, problem with paper receipt","id_opposite_semantic_model":null,"category":"price","aspect":"payment","feature":"bill problem","polarity":-1,"segment":"However, we did have some issues with the payment because they only accepted cash"},"5":{"id_semantic_model":72,"name_semantic_model":"hostel_adequate_price","description":"The hostel was reasonably priced, good price, hostel adequate priced, good deal, worth the money, good value, good experience for the price","id_opposite_semantic_model":71,"category":"price","aspect":"payment","feature":"adequate price","polarity":1,"segment":"Despite this, the hostel offered good value for the money"},"6":{"id_semantic_model":226,"name_semantic_model":"overall_good_satisfied","description":"The customer was satisfied in general, good overall, nice stay, good experience, good place, enjoyable experience, everything we needed, met my expectations","id_opposite_semantic_model":228,"category":"hostel","aspect":"opinion","feature":"overall good, satisfied","polarity":2,"segment":"Overall, it was a good choice for a one night stay"}},"semantic_summary":{"0":"The hostel is close to the airport","1":"The customer was satisfied in general","2":"Check-in/out was slow","3":"The common area was available","4":"There was a problem with the bill/payment","5":"The hostel was reasonably priced"}}
curl --location --request POST 'https://zylalabs.com/api/877/semantic+analysis+for+hostel+reviews+api/664/analysis?text=We only stayed at this hostel for one night because it was convenient to the airport. The check-in process took 30 minutes, which frustrated us because there were a lot of people checking in at the same time. Besides that, our experience at the hostel was positive. The shared bathrooms were not too crowded and the common room had billiards and ping-pong. However, we did have some issues with the payment because they only accepted cash. Despite this, the hostel offered good value for the money. Overall, it was a good choice for a one-night stay.' --header 'Authorization: Bearer YOUR_API_KEY'
After signing up, every developer is assigned a personal API access key, a unique combination of letters and digits provided to access to our API endpoint. To authenticate with the Semantic Analysis for Hostel Reviews API simply include your bearer token in the Authorization header.
| Header | Description |
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Authorization
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Required
Should be Bearer access_key. See "Your API Access Key" above when you are subscribed.
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About the API:
The Semantic Analysis for Hostel Reviews API allows users to analyze customer reviews of hostels and retrieve information about the key themes and sentiments expressed in the reviews. The API returns a list of keywords and phrases, as well as a sentiment score for each review, with a score of 1 indicating a positive review and a score of -1 indicating a negative review. This API is useful for hostel owners looking to understand the key themes and sentiments expressed in customer reviews and for travelers looking to get a sense of the overall sentiment of reviews for a particular hostel.
Pass the text that you want to analyze and receive the different intentions within it with the sentiment score.
A hostel booking website or app could use the API to provide users with a summary of the key themes and sentiments expressed in reviews for a particular hostel.
A hostel management platform could use the API to help hostel owners understand the key issues and areas of satisfaction mentioned in customer reviews, in order to improve their business.
A travel website or app could use the API to help tourists get a sense of the overall quality and reputation of a hostel before booking a stay.
A hostel review website or app could use the API to automatically categorize and summarize customer reviews for display on the website or app.
A market research firm could use the API to gather and analyze customer reviews for a range of hostels in order to track trends and identify key drivers of customer satisfaction.
A customer service platform could use the API to automatically classify and route customer reviews to the appropriate department or team for follow-up.
Besides the number of API calls per month, there is a limitation of 1 request per second.
The Analysis endpoint returns a comprehensive output that includes general sentiment, categories, aspects, semantic analysis, and a semantic summary of the provided review text.
Key fields in the response include "general_sentiment," "categories" (with names and sentiment scores), "aspects" (with names and sentiment scores), and "semantic_analysis" (detailing specific sentiments and descriptions).
The response data is structured as a JSON object, with nested objects for categories, aspects, and semantic analysis, allowing users to easily access detailed sentiment and thematic information.
The Analysis endpoint provides insights into customer sentiments, key themes, and specific aspects of hostel reviews, such as location, service, and price, helping users understand overall customer satisfaction.
Users can customize their requests by providing different review texts for analysis, allowing the API to return tailored insights based on the specific content of each review.
"general_sentiment" indicates overall sentiment (positive or negative), "categories" represent themes like "location" or "service," and "aspects" provide detailed sentiments related to specific features mentioned in the reviews.
Data accuracy is maintained through advanced semantic analysis algorithms that evaluate the context and sentiment of reviews, ensuring reliable insights into customer opinions and experiences.
Typical use cases include summarizing reviews for booking platforms, helping hostel owners identify improvement areas, and enabling travelers to gauge hostel quality before making reservations.
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