The Eurosentiment format is an extension of the NIF format data model for use in Sentiment Analysis. However, NIF and the Eurosentiment differ in one respect: Eurosentiment sets JSON-LD as its primary serialisation format, whereas NIF defaults to XML+RDF or turtle. It includes properties from Marl, Onyx and other ontologies that complement those in NIF for sentiment and emotion tagging. However, NIF and the Eurosentiment differ in one respect: Eurosentiment sets JSON-LD as its primary serialisation format, whereas NIF defaults to XML+RDF or turtle.
JSON-LD is a subset of JSON that makes it possible to embed semantic information in plain JSON objects. It retains full compatibility with JSON while adding useful information.
By using this serialisation format, Eurosentiment targets both semantic web developers and traditional developers alike.
{
"@context": [
"http://demos.gsi.dit.upm.es/eurosentiment/static/context.jsonld",
],
"@id": {{ processID }},
"analysis": [
{
"@id": {{ analysisID }},
"@type": [
{{ analysisType }}
],
"prov:wasAssociatedWith": {{ agent }},
"dc:language": {{ language}},
"marl:maxPolarityValue": {{ minValue }},
"marl:minPolarityValue": {{ maxValue }}
}
[...]
],
"domain": {{ domain }},
"entries": [
{
"@id": {{ entry_id }},
"dc:subject": {{ topic }},
"emotions": [
{
"prov:generatedBy": {{ analysisID }},
"onyx:hasEmotion": [
{
"onyx:hasEmotionCategory": {{ emotions[i].category }},
"onyx:intensity": {{ emotions[i].emotion_intensity }}
},
[...]
]
}
[...]
],
"opinions": [
{
"prov:generatedBy": {{ analysisID }},
"marl:polarityValue": {{ opinions[i].polarityValue }},
"marl:hasPolarity": {{ opinions[i].polarity }},
"marl:describesObject": {{ opinions[i].described_object }},
},
[...]
],
"nif:isString": {{ string_representation }},
"strings": [
{
"nif:anchorOf": {{ strings[i].value }},
"itsrdf:taIdentRef": {{ strings[i].entity }},
"nif:posTag": {{ strings[i].posTag }},
"nif:lemma": {{ strings[i].lemma }}
},
[...]
]
},
[...]
]
}- processID
Is the ID of the process that gathered the results.
- domain
Domain detected in the entries, or used by the analysis
- analysis
A set of results can be produced by combining the results from several analysis processes. Each of them needs to be described here.
- analysisID
Each of the analysis needs an unique URI so that the generated opinions/emotions can be linked to it. A set of results may aggregate the results from independent analysis (e.g. a sentiment analysis and an emotion analysis)
- analysisType
Example: marl:SentimentAnalysis or onyx:EmotionAnalysis
- algorithm
[In marl] Algorithm that was used to generate the results
- agent
Responsible for or creator of the analysis
- language
Language that the analysis uses. e.g. "es"
- minValue
[In marl opinions] Minimum value of the opinion value
- maxValue
[In marl opinions] Maximum value of the opinion value
- domain
Domain where the analysis was run. e.g. wnd:electronics
- entry_id
Each entry must have a unique URI
- topic
The subject or subjects of the entry. e.g. wnd:electronics
- emotions
The emotions found in the context. Depending on the theory of emotions used, emotions can be categorised and/or be defined by different dimensions. This example represents the usual case which is a model using categories.
- category
Category of the emotion. e.g. wna:Hatred
- emotion_intensity
Intensity of the emotion as defined by the algorithm
- opinions
The opinions found in the context.
- polarity
Polarity of the opinion. e.g. marl:Positive
- polarityValue
Numerical value of the polarity, as a floating point
- described_object
Object that the opinion is about
- string_representation
Plain text representation
- strings
A NIF context can be subdivided in substrings, which have their own properties. This is usually done to associate a particular string with an entity in Named Entity Recognition
- strings[i].value
Text representation
- strings[i].entity
Entity the string represents
- strings[i].posTag
Part-of-speech tag
- strings[i].lemma
Lemma of the word
The JSON-LD context contains semantic information about the properties in the JSON document, including convenient prefixes or namespaces. The Eurosentiment context would look like this:
{
"@context": {
"dc": "http://purl.org/dc/terms/",
"dc:subject": {
"@type": "@id"
},
"emotions": {
"@container": "@list",
"@id": "onyx:hasEmotionSet",
"@type": "onyx:EmotionSet"
},
"marl": "http://www.gsi.dit.upm.es/ontologies/marl#",
"nif": "http://persistence.uni-leipzig.org/nlp2rdf/ontologies/nif-core#",
"onyx": "http://www.gsi.dit.upm.es/ontologies/onyx#",
"opinions": {
"@container": "@list",
"@id": "marl:hasOpinion",
"@type": "marl:Opinion"
},
"prov": "http://www.w3.org/ns/prov#",
"rdfs": "http://www.w3.org/2000/01/rdf-schema#",
"analysis": {
"@id": "prov:wasInformedBy"
},
"entries": {
"@id": "prov:generated"
},
"strings": {
"@reverse": "nif:hasContext",
"@type": "nif:String"
},
"wnaffect": "http://www.gsi.dit.upm.es/ontologies/wnaffect#",
"xsd": "http://www.w3.org/2001/XMLSchema#"
}
}* Annotating one entry using a fictitious service (http://example.com/analyse) provided by http://example.com. Input: "My ipad is an awesome device". .. code-block:: javascript
- {
- "@context": [
"http://demos.gsi.dit.upm.es/eurosentiment/static/context.jsonld"
], "results": { "analysis": [ { "@id": "http://example.com/analyse", "@type": [ "marl:SentimentAnalysis" ], "dc:language": "en", "marl:maxPolarityValue": 10.0, "marl:minPolarityValue": 0.0 "prov:wasAssociatedWith": "http://example.com" } ], "entries": [ { "@id": "http://example.com/analyse?input=My%20ipad%20is%20an%20awesome%20device", "opinions": [ { "marl:polarityValue": 9, "marl:hasPolarity": "marl:Positive", "marl:describesObject": "http://dbpedia.org/page/IPad" "prov:generatedBy": "http://example.com/analyse", } ], "nif:isString": "My ipad is an awesome device", "strings": [ { "@id": "http://example.com/analyse?input=My%20ipad%20is%20an%20awesome%20device#char=3,6", "nif:anchorOf": "ipad", "itsrdf:taIdentRef": "http://dbpedia.org/page/IPad" } ] } ] }
}
* Annotating complex emotions in Spanish. Input: "Mi ipad me tiene harto". .. code-block:: javascript
- {
- "@context": [
"http://demos.gsi.dit.upm.es/eurosentiment/static/context.jsonld"
], "results": { "analysis": [ { "@id": "http://example.com/analyse", "@type": [ "onyx:EmotionAnalysis" ], "dc:language": "es", "onyx:maxEmotionIntensity": 1.0, "onyx:minEmotionIntensity": 0.0 "prov:wasAssociatedWith": "http://example.com/" } ], "entries": [ { "@id": "http://example.com/analyse?input=Mi%20ipad%20me%20tiene%20harto", "dc:language": "es", "opinions": [ ], "emotions": [ { "onyx:aboutObject": "http://dbpedia.org/page/IPad" "prov:generatedBy": "http://example.com/analyse", "onyx:hasEmotion": [ { "onyx:hasEmotionCategory": "wna:dislike", "onyx:hasEmotionIntensity": 0.7 }, { "onyx:hasEmotionCategory": "wna:despair", "onyx:hasEmotionIntensity": 0.1 } ] } ], "nif:isString": "My ipad is an awesome device", "prov:generatedBy": "http://example.com/analyse", "strings": [ { "@id": "http://example.com/analyse?input=Mi%20ipad%20me%20tiene%20harto#char=3,6", "nif:anchorOf": "ipad", "itsrdf:taIdentRef": "http://dbpedia.org/page/IPad" } ] } ] }
}
The Eurosentiment format is semantic, as is the NIF Format Althought the preferred and mainly used serialisation format is JSON-LD, there are other serialisation formats that could be used as well.
For instance, it is particularly interesting to convert corpora to N-Triples for storage in a semantic server such as Virtuoso.