Skip to main content

Access SenticNet data using Python

Project description

SenticNet API


Simple API to use SenticNet (


Using pip:

$ pip install senticnet

Using the repository code:

$ python install

How to use

from senticnet.senticnet import SenticNet

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

Also, you can use other languages:

from senticnet.babelsenticnet import BabelSenticNet

bsn = BabelSenticNet('pt')
concept_info = sn.concept('amor')
polarity_label = sn.polarity_label('amor')
polarity_value = sn.polarity_value('amor')
moodtags = sn.moodtags('amor')
semantics = sn.semantics('amor')
sentics = sn.sentics('amor')

You can find all supported languages here:

About SenticNet

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 6 in any research work or presentation containing results obtained in whole or in part through the use of the API:

E Cambria, Y Li, F Xing, S Poria, K Kwok. SenticNet 6: Ensemble application of symbolic and subsymbolic AI for sentiment analysis. In: CIKM, 105-114 (2020)

Project details

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

senticnet-1.6.tar.gz (50.2 MB view hashes)

Uploaded Source

Built Distribution

senticnet-1.6-py3-none-any.whl (51.9 MB view hashes)

Uploaded Python 3

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page