Sentiment Classification Using Document Embeddings Trained with Cosine Similarity

In document-level sentiment classification, each document must be mapped to a fixed length vector. Document embedding models map each document to a dense, low-dimensional vector in continuous vector space... (read more)

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Results from the Paper


 Ranked #1 on Sentiment Analysis on IMDb (using extra training data)

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TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK USES EXTRA
TRAINING DATA
BENCHMARK
Sentiment Analysis IMDb NB-weighted-BON + dv-cosine Accuracy 97.4 # 1

Methods used in the Paper


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