Papers by Tanasanee Phienthrakul
Sentiment Classification Using Document Embeddings Trained with Cosine Similarity (P19-2)
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| Challenge: | Existing document embedding models map each document to a dense, low-dimensional vector in continuous vector space. |
| Approach: | They propose to train document embeddings using cosine similarity instead of dot product . they focus on document sentiment classification of long movie reviews . |
| Outcome: | The proposed model improves document embedding accuracy by using cosine similarity instead of dot product on the IMDB dataset while using feature combination with Naive Bayes weighted bag of n-grams achieves 93.68% accuracy. |