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.
(1) a positive correlation exits between preference and recognition, and users are more willing to buy the cars with higher degree of recognition; (2) in the process of car brand recognition, customers use its working memory to process the visual information of styling and extract the car model information in his long-term memory for comparative analysis, so both visual stimulation intensity and continuity should concurrently be taken into account in car styling design, thus the short-term memory shall be transformed into long-term memory
styling and extract
recognition customers
stimulation intensity
higher degree
model information
higher
positive
This document is:
positive (+0.65)
Magnitude: 0.85
Subjectivity: subjective
Subjectivity: subjective
Score Range
negative neutralpositive
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|
 |
| Detected Themes | Magnitude | Sentiment Score |
|---|---|---|
| model information | 0.99 | +0.736 |
| higher degree | 0.98 | +0.706 |
| stimulation intensity | 0.68 | +0.588 |
| recognition customers | 0.84 | +0.569 |
| styling and extract | 0.61 | +0.508 |
| Detected Keywords | Magnitude | Sentiment Score |
|---|---|---|
| positive | 0.979 | +0.739 |
| higher | 0.97 | +0.735 |
| transformed | 0.004 | +0.249 |
| willing | 0.129 | +0.218 |
| Core sentences | Magnitude | Sentiment Score |
|---|---|---|
| 1 a positive correlation exits between preference and recognition and users are more willing to buy the cars with higher degree of recognition 2 in the process of car brand recognition customers use its working memory to process the visual information of styling and extract the car model information in his long term memory for comparative analysis so both visual stimulation intensity and continuity should concurrently be taken into account in car styling design thus the short term memory shall be transformed into long term memory | 0.85 | +0.603 |
Auto categories [IAB QAG taxonomy]
| Category | Score |
|---|---|
| Technology & Computing/ Consumer Electronics | 0.296 |
| Technology & Computing/ Computing/ Computer Software & Applications | 0.263 |
| Medical Health | 0.168 |
| Automotive | 0.117 |
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 ∞.
