The Semantic Analysis for Apartment Reviews API is a tool that allows users to analyze and extract insights from apartment reviews. It uses natural language processing and machine learning techniques to understand the sentiment and meaning behind written reviews and provides a range of features for analyzing and summarizing this data.
This API can be used by apartment complex managers and owners to understand better what tenants like and dislike about their properties and identify areas for improvement. It can also be used by researchers or journalists to quickly and easily analyze large volumes of reviews and extract insights.
The API will receive a text that is intended to be a review of an apartment. The API will analyze the content and will extract the semantics and sentiments of the text. It will determine if the review is positive or negative, among other things.
Apartment complex managers and owners can use the API to get a better understanding of tenant feedback and identify areas for improvement. For example, if a high number of negative reviews mention the same issue (such as noisy neighbors or a lack of parking), the complex can address this issue to improve tenant satisfaction.
Researchers or journalists can use the API to quickly and easily analyze large volumes of apartment reviews and extract insights. This can be useful for studying trends in the rental market or for creating reports on the quality of different apartment complexes.
Marketing agencies or consulting firms can use the API to help their clients understand the sentiment and content of their reviews, and identify areas for improvement.
Real estate agents or brokers can use the API to analyze reviews of properties they are selling or renting, and use this information to market the properties more effectively.
Consumer advocacy groups or government agencies can use the API to monitor and report on the quality of rental properties in a given area.
The only limitation is the number of API calls you can make per month that support each plan.
Pass the text that you want to analyze and receive the extracted intents and its score.
Semantic Analysis - Endpoint Features
Object | Description |
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text |
[Required] Text to analyze. |
{"general_sentiment":0,"categories":{"0":{"name":"location","count":4,"sentiment_score":0.25},"1":{"name":"price","count":1,"sentiment_score":1},"2":{"name":"apartment","count":3,"sentiment_score":-1},"3":{"name":"addons","count":2,"sentiment_score":0.5}},"aspects":{"0":{"name":"close to","count":3,"sentiment_score":1},"1":{"name":"payment","count":1,"sentiment_score":1},"2":{"name":"bed","count":1,"sentiment_score":-2},"3":{"name":"apartment","count":1,"sentiment_score":1},"4":{"name":"wifi","count":1,"sentiment_score":2},"5":{"name":"parking","count":1,"sentiment_score":-1},"6":{"name":"neighbourhood","count":1,"sentiment_score":-2},"7":{"name":"opinion","count":1,"sentiment_score":-2}},"semantic_analysis":{"0":{"id_semantic_model":178,"name_semantic_model":"close_to_center","description":"The apartment is close to the center, short distance, easy walk downtown, city centre, town, main street","id_opposite_semantic_model":346,"category":"location","aspect":"close to","feature":"center","polarity":1,"segment":"We got this apartment because of its cheap price and near to the centre"},"1":{"id_semantic_model":214,"name_semantic_model":"apartment_low_price_cheap","description":"The apartment price was low, apartment was cheap, inexpensive, low price, budget rate, good for budget stay","id_opposite_semantic_model":null,"category":"price","aspect":"payment","feature":"inexpensive","polarity":1,"segment":"We got this apartment because of its cheap price and near to the centre"},"2":{"id_semantic_model":50,"name_semantic_model":"bed_uncomfy","description":"The bed was not comfortable, mattress, bedding not comfortable, not good, hard, need replacement, too firm, too soft, need new bed, new mattress","id_opposite_semantic_model":45,"category":"apartment","aspect":"bed","feature":"uncomfy","polarity":-2,"segment":"The bedroom was a little bit small and the bed wasn't that comfortable"},"3":{"id_semantic_model":38,"name_semantic_model":"apartment_decor_nice","description":"The apartment decor was tasteful and elegant, modern nice, tasteful, elegant decor, nice style","id_opposite_semantic_model":8,"category":"apartment","aspect":"apartment","feature":"nice decor","polarity":1,"segment":"There was a lot of lovely decorations and it was nice"},"4":{"id_semantic_model":99,"name_semantic_model":"wifi_work_good","description":"Wi-Fi was fast and reliable, steady signal, strong signal, good connection, no complaints","id_opposite_semantic_model":97,"category":"addons","aspect":"wifi","feature":"works good","polarity":2,"segment":"There was very fast wifi, which was great"},"5":{"id_semantic_model":123,"name_semantic_model":"parking_lack_of","description":"Lack of parking, no parking, no car parking","id_opposite_semantic_model":260,"category":"addons","aspect":"parking","feature":"lack of","polarity":-1,"segment":"However there was no parking spaces in front of the house so we spend some time sechrching for a place"},"6":{"id_semantic_model":164,"name_semantic_model":"neighborhood_not_safe","description":"The apartment neighborhood was sketchy, location not safe, scary, dangerous","id_opposite_semantic_model":203,"category":"location","aspect":"neighbourhood","feature":"not safe","polarity":-2,"segment":"Also the neigherhood was full of drunken people, probably it was because the house wasn't that far from downtown and beach and there was a lot of homeless people"},"7":{"id_semantic_model":178,"name_semantic_model":"close_to_center","description":"The apartment is close to the center, short distance, easy walk downtown, city centre, town, main street","id_opposite_semantic_model":346,"category":"location","aspect":"close to","feature":"center","polarity":1,"segment":"Also the neigherhood was full of drunken people, probably it was because the house wasn't that far from downtown and beach and there was a lot of homeless people"},"8":{"id_semantic_model":183,"name_semantic_model":"close_to_beach","description":"The apartment is close to the beach, short distance, easy walk to beach","id_opposite_semantic_model":null,"category":"location","aspect":"close to","feature":"beach","polarity":1,"segment":"Also the neigherhood was full of drunken people, probably it was because the house wasn't that far from downtown and beach and there was a lot of homeless people"},"9":{"id_semantic_model":162,"name_semantic_model":"will_not_return","description":"The customer will not return to this apartment, will not come back, never coming back, not stay again, not likely to return, never coming back, doubt coming back","id_opposite_semantic_model":172,"category":"apartment","aspect":"opinion","feature":"will not return","polarity":-2,"segment":"won't come back"}},"semantic_summary":{"0":"The apartment is close to the center","1":"The apartment price was low","2":"The bed was not comfortable","3":"The apartment decor was tasteful and elegant","4":"Wi-Fi was fast and reliable","5":"Lack of parking","6":"The apartment neighborhood was sketchy","7":"The apartment is close to the beach","8":"The customer will not return to this apartment"}}
curl --location --request POST 'https://zylalabs.com/api/866/semantic+analysis+for+apartment+reviews+api/646/semantic+analysis?text=We got this apartment because of its cheap price and near to the centre. The bedroom was a little bit small and the bed was not that comfortable. There was a lot of lovely decorations and it was nice. There was very fast wifi, which was great. However there was no parking spaces in front of the house so we spend some time sechrching for a place. Also the neigherhood was full of drunken people, probably it was because the house was not that far from downtown and beach and there was a lot of homeless people Will not come back.' --header 'Authorization: Bearer YOUR_API_KEY'
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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