This project describes HappyDB and its properties and outlines several important NLP problems that can be studied with the help of the corpus. Additionally, this paper tries to incorporate the dataset as a reference for Work-Life-Balance in the time of a pandemic as COVID19. Our results demonstrate the need for deeper NLP techniques to be developed which makes HappyDB an exciting resource for follow-on research
├── ProjectReport.pdf
├── README.md
├── dataset
│ ├── Classes.csv
│ ├── cleaned_hm.csv
│ ├── demographic.csv
│ └── demographic_cleaned.csv
└── notebooks
├── Decision_Tree_happy_DB.ipynb
├── HappyDB_Data_Analysis.ipynb
├── HappyDB_Merging_Pre_processing.ipynb
├── RandomForest_happy_DB.ipynb
└── Source_Dataset_information.ipynb
Akari Asai, Sara Evensen, Behzad Golshan, Alon Halevy, Vivian Li, Andrei Lopatenko,
Daniela Stepanov, Yoshihiko Suhara, Wang-Chiew Tan, Yinzhan Xu,
``HappyDB: A Corpus of 100,000 Crowdsourced Happy Moments'', LREC '18, May 2018. (to appear)
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