Our demo service uses generic models trained on real user's comments, product, service opinions. In order to get specific results that are tailored to your domain, please consider training your own sentiment model.
Please enter your text in english* for analysis or leave default one.
Have stayed here off and on over several years. great location - access to trains, underground and buses plus good walking distance to Westminster. l However, as a single traveller end up paying for small, narrow room. Room was adequate, bathroom with shower stall very cramped. A bit disappointed this time, but since I really like the location I will go back. Did not have much interaction with staff.
great location access
buses plus
very cramped
good walking distance
disappointed
good
cramped
great
This document is:
negative (-0.61)
Magnitude: 3.99
Subjectivity: subjective
Subjectivity: subjective
Score Range
negative neutralpositive
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| Detected Entities | Type | Magnitude | Sentiment Score |
|---|---|---|---|
| Westminster | LOC | 0.07 | +0.232 |
| Detected Themes | Magnitude | Sentiment Score |
|---|---|---|
| very cramped | 0.98 | -0.719 |
| buses plus | 0.97 | +0.690 |
| great location access | 0.81 | +0.602 |
| good walking distance | 0.58 | +0.898 |
| Detected Keywords | Magnitude | Sentiment Score |
|---|---|---|
| cramped | 0.913 | -0.976 |
| disappointed | 0.974 | -0.735 |
| narrow | 0.001 | -0.250 |
| single | 0.002 | -0.250 |
| several | 0.003 | -0.249 |
| small | 0.004 | -0.249 |
| stall | 0.004 | -0.249 |
| good | 0.988 | +0.743 |
| great | 0.978 | +0.992 |
| Core sentences | Magnitude | Sentiment Score |
|---|---|---|
| Have stayed here off and on over several years. | 0.29 | 0.178 |
| Great location access to trains underground and buses plus good walking distance to Westminster. | 0.92 | +0.630 |
| l However as a single traveller end up paying for small narrow room. | 0.56 | +0.110 |
| Room was adequate bathroom with shower stall very cramped. | 0.72 | -0.433 |
| A bit disappointed this time but since I really like the location I will go back. | 0.73 | -0.539 |
| Did not have much interaction with staff. | 0.78 | -0.563 |
Auto categories [IAB QAG taxonomy]
| Category | Score |
|---|---|
| Hotels and Motels | 1.000 |
No user categories detected
About analysis
- Once analysis is finished, you will see the overall score for the document and extracted thematic concepts, entities, keyword phrases, auto categories..
- In longer documents, entity/theme sentiment is in general more useful.
- The better input text is formatted (properly placed commas, spaces between sentences etc.), the faster and more accurate analysis will be returned.
- Twitter mode is usually more accurate for short, unformatted contents. Includes irony, slang and abbreviation detection. In this mode additional text cleaning is performed, inluding removal of usernames (starting from @), links, numbers and special characters. Hashtags are being left for analysis.
- Magnitude is the volume of sentiment expressed regardless of sentiment polarity, it can be used to detect strength of emotions or fine-tune sentiment polarity. It ranges from 0 to ∞.
