Sentiment analysis output
The sentiment analysis resource returns a JSON object with this format:
{
"success": Boolean success flag,
"data": {
"content": analyzed text,
"language": language code,
"version": technology version info,
"knowledge": [],
"sentiment": []
}
}
For the description of the contents, language and version properties, see the API resources output overview.
The knowledge array contains Knowledge Graph data about items of the sentiment array, as a result of the semantic analysis process. Its contents are described in the article about the output of full analysis.
sentiment
The sentiment object contains three scores indicative of the tone of the whole text:
positivity: the amount of positivitynegativity: the amount of negativityoverall: the overall sentiment score, which is a combination of the scores above
All sentiment scores are expressed in a -100 (extremely negative) to 100 (extremely positive) range.
The sentiment object contains an items array whose elements, in turn, can contain nested items arrays. These items represent the clusters of text elements that give a positive or negative contribution to sentiment.
For example, given this input text:
The road was bad.
items clusters can be like this:
"items": [
{
"lemma": "road",
"sentiment": -7,
"syncon": 19001,
"items": [
{
"lemma": "bad",
"sentiment": -7,
"syncon": 81195
}
]
}
]
sentiment is the sentiment score of the cluster or leaf-item. The sentiment score of a cluster is a function of the child items' scores and the the possible modifiers, which are not returned as separate item, but are nevertheless taken into account.
Take, for example, a slight change introduced in the sample text:
The road was really bad.
the really modifier makes the score get worse:
"items": [
{
"lemma": "road",
"sentiment": -8.8,
"syncon": 19001,
"items": [
{
"lemma": "bad",
"sentiment": -8.8,
"syncon": 81195
}
]
}
]
On the other hand, a not before bad can invert the sentiment polarity from negative to positive. The sentiment value can be zero.
The syncon and lemma properties are the outcome of semantic analysis and lemmatization respectively. These are the same processes carried out during deep linguistic analysis, but the value of lemma can have some peculiarities.
An item having nested items can be an "unnamed cluster": in that case the lemma property is an empty string.
If the intrinsic item polarity—positive or negative—is opposite of that of the paragraph it belongs to, this marker:
[*]
is added as a suffix to the the lemma.
For example, given this input text:
The road was not bad.
The lemma for bad is marked with the "opposite polarity" sign because it is negated by not:
"items": [
{
"items": [
{
"lemma": "bad[*]",
"sentiment": 7,
"syncon": 87597
}
],
"lemma": "road",
"sentiment": 7,
"syncon": 19001
}
]
Another possibility is that a lemma "attracts" other words in the same phrase. For example, given the input text:
Michael Jordan was one of the best basketball players of all time. Scoring was Jordan's stand-out skill, but he still holds a defensive NBA record, with eight steals in a half.
a value of lemma could be:
stand-out;skill
In this case the merged terms are separated by a semi-colon (;).
Value -1 for syncon means the concept doesn't have a correspondent in the expert.ai Knowledge Graph.
knowledge
The knowledge array contains Knowledge Graph data for the elements of the items array. Its contents are described in the article about the output of full analysis.