Sentiment analysis using doc2vec embeddings
Text classification requires combining multiple word embeddings. A common approach is to average the embedding vectors for each word in the document. This uses information from all embeddings and effectively uses vector addition to arrive at a different location point in the embedding space. However, relevant information about the order of words is lost.
In contrast, the document embedding model, doc2vec, developed by the word2vec authors shortly after publishing their original contribution, produces embeddings for pieces of text like a paragraph or a product review directly. Similar to word2vec, there are also two flavors of doc2vec:
- The distributed bag of words (DBOW) model corresponds to the word2vec CBOW model. The document vectors result from training a network on the synthetic task of predicting a target word based on both the context word vectors and the document's doc vector.
- The distributed memory (DM)...
