Simple API to use SenticNet 5 (http://sentic.net/).

Using pip:

$ pip install senticnet

Using the repository code:

$ python setup.py install

from senticnet.senticnet import SenticNet

sn = SenticNet()
concept_info = sn.concept('love')
polarity_value = sn.polarity_value('love')
polarity_intense = sn.polarity_intense('love')
moodtags = sn.moodtags('love')
semantics = sn.semantics('love')
sentics = sn.sentics('love')

Also, you can use other languages:

from senticnet.senticnet import SenticNet

sn = SenticNet('pt')
concept_info = sn.concept('amor')
polarity_value = sn.polarity_value('amor')
polarity_intense = sn.polarity_intense('amor')
moodtags = sn.moodtags('amor')
semantics = sn.semantics('amor')
sentics = sn.sentics('amor')

You can find all supported languages here: http://sentic.net/api/

SenticNet is an initiative conceived at the MIT Media Laboratory in 2010 within an industrial Cooperative Awards in Science and Engineering (CASE) research project, funded by the UK Engineering and Physical Sciences Research Council (EPSRC) and born from the collaboration between the University of Stirling, the Media Lab, and Sitekit Labs.

Currently, both the SenticNet knowledge base and the SenticNet framework are being maintained and further developed by the Sentic Team, a multidisciplinary research group based at the School of Computer Engineering of Nanyang Technological University in Singapore, but also by many other sentic enthusiasts around the world.

Please acknowledge the authors by citing SenticNet 5 in any research work or presentation containing results obtained in whole or in part through the use of the API:

E Cambria, S Poria, D Hazarika, K Kwok. SenticNet 5: Discovering conceptual primitives for sentiment analysis by means of context embeddings. In: AAAI, pp. 1795-1802 (2018)