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MuseumRecommendation/Data/review_quote_world.json at master · annecool37/MuseumRecommendation · GitHub
dexter-datasets/entity-saliency/saliency-dataset.json at master · dexter/dexter-datasets · GitHub
Emojify/tweets.txt at master · yzan424/Emojify · GitHub
This project is an experiment to find if a tweet is sarcastic and Brown clustering of similar words, topics association with sarcasm - used to generate feature vectors, POS - tagging, n-gram feature vector generation, sentiment analysis of the tweets and scoring based on positivity(negativity) of the words in a tweet to find the mix of positive sentiment and negative actions are some of the techniques used to identify sarcasm. The 'kitchen sink' of a mix of these features helped us achieve close to ~58% accuracy. Languages - JAVA, Python, iPython - NLP_Sarcasm_Detection/normal_tweets.csv at master · deepakkumar-b/NLP_Sarcasm_Detection
NLP_Sarcasm_Detection/normal_tweets.csv at master · deepakkumar-b/NLP_Sarcasm_Detection · GitHub
Latent Dialogue Model with Answer Clustering. Contribute to KevinFang97/ano development by creating an account on GitHub.
ano/test/data/vocab.json at master · KevinFang97/ano · GitHub
Detecting whether a comment can be insulting to a person on a social forum or not. Kaggle challenge. - Detect-Insults-in-Social-commentary/train.csv at master · saynb/Detect-Insults-in-Social-commentary
Detect-Insults-in-Social-commentary/train.csv at master · saynb/Detect-Insults-in-Social-commentary · GitHub