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Unlike many other sentiment lexica, SenticNet is not built by manually labeling pieces of knowledge coming from general NLP resources such as WordNet or DBPedia. Instead, it is automatically constructed by applying graph-mining and multi-dimensional scaling techniques on the affective commonsense knowledge collected from three different sources, namely: WordNet-Affect, Open Mind Common Sense and GECKA. This knowledge is represented redundantly at three levels (following Minsky's panalogy principle): semantic network, matrix, and vector space. Subsequently, semantics and sentics are calculated through the ensemble application of spreading activation, sentic neurons and the Hourglass of Emotions.

SenticNet

The different versions of SenticNet are accessible in RDF/XML format: SenticNet 1 (2010) simply associated polarity scores with almost 6,000 ConceptNet concepts; in addition to polarity, SenticNet 2 (2012) also assigned semantics and sentics to commonsense concepts and extended the breadth of the knowledge base to about 13,000 entries; SenticNet 3 (2014) broadened the spectrum of the semantic network to 30,000 concepts; SenticNet 4 (2016) introduced the concept of semantic primitives to further extend the knowledge base to 50,000 entries; SenticNet 5 (2018) reached 100,000 commonsense concepts by employing recurrent neural networks to infer primitives by lexical substitution; SenticNet 6 (2020) used both symbolic models, e.g., logic, and subsymbolic methods, e.g., transformers, to further extend the size of the knowledge base to 200,000 words and multiword expressions; SenticNet 7 (2022) leveraged a novel commonsense-based neurosymbolic AI framework for explainable sentiment analysis to assign semantics and sentics to 300,000 concepts; finally, SenticNet 8 (2024) fuses emotion AI and commonsense AI for interpretable, trustworthy, and explainable affective computing. We do not recommend using older versions of SenticNet but, if you really need to do so, you can access them at https://sentic.net/senticnet-$.0.zip (replace $ sign with version number). The latest version of the knowledge base can also be downloaded in OWL format as an ontology and selectively accessed online through an API, available in 80 different languages. Each language-dependent version of SenticNet is also downloadable in RDF/XML format as BabelSenticNet.

senticnet

SenticNet is also available in the form of embeddings as AffectiveSpace, the 100-dimensional vector space representation of the 200,000 concepts that make up the affective commonsense knowledge graph. Another vector space model available for download is PrimeNet, a strongly-connected core of hyponym-hypernym common knowledge. We also make some code available on our GitHub account and offer a suite of APIs that perform various sentiment analysis tasks. In case none of these work for you, feel free to send us your data and we will do the classification for you.


ENGLISH RESOURCES
SenticNet
AffectiveSpace
OntoSenticNet
PrimeNet

CHINESE RESOURCES
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ARABIC RESOURCES
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SPANISH RESOURCES
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PORTUGUESE RESOURCES
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FRENCH RESOURCES
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GERMAN RESOURCES
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ITALIAN RESOURCES
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RUSSIAN RESOURCES
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TURKISH RESOURCES
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HINDI RESOURCES
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BENGALI RESOURCES
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TAMIL RESOURCES
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URDU RESOURCES
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PUNJABI RESOURCES
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PERSIAN RESOURCES
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PASHTO RESOURCES
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MALAY RESOURCES
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INDONESIAN RESOURCES
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JAPANESE RESOURCES
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KOREAN RESOURCES
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VIETNAMESE RESOURCES
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THAI RESOURCES
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BURMESE RESOURCES
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TAGALOG RESOURCES
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POLISH RESOURCES
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ROMANIAN RESOURCES
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DUTCH RESOURCES
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UZBEK RESOURCES
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HAUSA RESOURCES
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AFRIKAANS RESOURCES
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HEBREW RESOURCES
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GREEK RESOURCES
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SWEDISH RESOURCES
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DANISH RESOURCES
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FINNISH RESOURCES
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NORWEGIAN RESOURCES
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ICELANDIC RESOURCES
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MONGOLIAN RESOURCES
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SERBIAN RESOURCES
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CROATIAN RESOURCES
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SLOVENIAN RESOURCES
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ALBANIAN RESOURCES
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BOSNIAN RESOURCES
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MACEDONIAN RESOURCES
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LAO RESOURCES
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BULGARIAN RESOURCES
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ARMENIAN RESOURCES
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KYRGYZ RESOURCES
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BELARUSIAN RESOURCES
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ESTONIAN RESOURCES
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LATVIAN RESOURCES
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WELSH RESOURCES
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GAELIC RESOURCES
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