Sap Se

Weile Chen

Hang Guo

Abraham Sasmito Adibowo

Xin Zheng

Ruidan He

Danqing Cai

Daniel Hermann Richard Dahlmeier

Subashini Rengarajan

Wenya Wang

Wei Tah Chai

Kham Sian Mung

Mohamed Yusuf Abdul Gafoor

Pek Gnee Ng

Yi Qing Isaac New

Haodan Yang

Described herein is a framework for classifying social media inputs. In accordance with one aspect of the framework, one or more social media inputs is acquired from one or more social media platforms. The social media inputs are cleaned to remove redundant elements. One or more features are extracted from the cleaned social media inputs. The social media inputs are classified by a trained classifier into predefined categories using the extracted one or more features.

Classifying social media inputs via parts-of-speech filtering

Described herein is a machine learning framework for facilitating engagements. In accordance with one aspect of the framework, a machine learning model is trained based on the training data. A recommendation associated with an opportunity record may then be generated using the trained machine learning model. Results of one or more actions performed in response to the recommendation may be collected and fed back to the machine learning model to be used as the training data.

Machine learning framework for facilitating engagements

A framework for improving data set in an enterprise system for machine learning is provided. In accordance with one aspect, user input of a project update is provided by a user to an enterprise system. A record of the project update is created in the enterprise system based on the user input. The project update provided by the user into the enterprise system is analyzed using a gamification technique. The analysis includes quantifying the user's input of the project update to the enterprise system by assigning points to the user based on the project update provided to the enterprise system. The assigned points are displayed to the user on a user interface of a user device to enable friendly competition with other users which encourages more detailed and frequent project updates to the enterprise system by the user.

Data input in an enterprise system for machine learning

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Singapore, Singapore

Weile Chen