Identify hotel reviews sentiment
using AI
Below is a free classifier to identify hotel reviews sentiment. Just input your text, and our AI will predict the sentiment of hotel reviews - in just seconds.
API Access
import nyckel
credentials = nyckel.Credentials("YOUR_CLIENT_ID", "YOUR_CLIENT_SECRET")
nyckel.invoke("hotel-reviews-sentiment", "your_text_here", credentials)
fetch('https://www.nyckel.com/v1/functions/hotel-reviews-sentiment/invoke', {
method: 'POST',
headers: {
'Authorization': 'Bearer ' + 'YOUR_BEARER_TOKEN',
'Content-Type': 'application/json',
},
body: JSON.stringify(
{"data": "your_text_here"}
)
})
.then(response => response.json())
.then(data => console.log(data));
curl -X POST \
-H "Content-Type: application/json" \
-H "Authorization: Bearer YOUR_BEARER_TOKEN" \
-d '{"data": "your_text_here"}' \
https://www.nyckel.com/v1/functions/hotel-reviews-sentiment/invoke
How this classifier works
To start, input the text that you'd like analyzed. Our AI tool will then predict the sentiment of hotel reviews.
This pretrained text model uses a Nyckel-created dataset and has 18 labels, including Awful, Bad, Dissatisfied, Excellent, Good, Great, Happy, Mixed, Negative and Neutral.
We'll also show a confidence score (the higher the number, the more confident the AI model is around the sentiment of hotel reviews).
Whether you're just curious or building hotel reviews sentiment detection into your application, we hope our classifier proves helpful.
Recommended Classifiers
Need to identify hotel reviews sentiment at scale?
Get API or Zapier access to this classifier for free. It's perfect for:
- Customer Satisfaction Monitoring: This function can be utilized by hotel managers to evaluate the sentiment of guest reviews, allowing them to gauge overall customer satisfaction. By analyzing positive, negative, and neutral sentiments, hotels can identify areas for improvement and enhance guest experiences.
- Targeted Marketing Campaigns: Hotels can leverage sentiment analysis to tailor marketing efforts based on guest feedback trends. By understanding which features or services are most appreciated or criticized, hotels can shape their promotional messages to align with customer sentiments, attracting potential guests more effectively.
- Competitive Analysis: Hotel chains can use sentiment classification to compare their reviews against competitors. By assessing the sentiment behind customer feedback across different businesses, they can identify competitive strengths and weaknesses, adjusting strategies accordingly to improve their market position.
- Service Improvement Initiatives: This functionality enables hotels to pinpoint specific aspects of service that elicit strong sentiments from guests. By focusing on areas with negative reviews, hotels can implement targeted improvements, ensuring a more satisfying stay for future guests.
- Automated Response Generation: Hotels can automate response generation to reviews based on sentiment analysis results. Positive reviews can receive enthusiastic responses, while negative reviews can be addressed with empathy and problem-solving offers, streamlining customer engagement efforts.
- Staff Training Programs: By analyzing sentiments in hotel reviews, management can identify recurring issues related to staff service. This information can be utilized to develop targeted training programs aimed at improving staff performance, thus enhancing the overall guest experience.
- Reputation Management: Sentiment classification can help in monitoring the hotel's online reputation over time. By consistently analyzing review sentiments, hotels can quickly identify any shifts in public perception and proactively address negative trends before they escalate.