Is there a rule for deciding dictionary size for sentiment analysis with massive datasets? Announcing the arrival of Valued Associate #679: Cesar Manara Planned maintenance scheduled April 17/18, 2019 at 00:00UTC (8:00pm US/Eastern) 2019 Moderator Election Q&A - Questionnaire 2019 Community Moderator Election ResultsWhat is the current state-of-the-art within aspect-based sentiment analysis?How to overcome training example's different lengths when working with Word Embeddings (word2vec)Features of word vectors in word2vecNLP - why is “not” a stop word?What are key dataset requirements for topic models and word embeddings?What is a lower bound on the vocabulary size for generating word/sentence embedding using word2vec or skip thought vectors?use hidden layer of word2vec instead of 'one-hot', to reduce number of weights in other nets?How to replace short words into full words from tweets using pythontransfer learning with sentiment analysis?How to learn irrelevant words in an information retrieval system?
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Is there a rule for deciding dictionary size for sentiment analysis with massive datasets?
Announcing the arrival of Valued Associate #679: Cesar Manara
Planned maintenance scheduled April 17/18, 2019 at 00:00UTC (8:00pm US/Eastern)
2019 Moderator Election Q&A - Questionnaire
2019 Community Moderator Election ResultsWhat is the current state-of-the-art within aspect-based sentiment analysis?How to overcome training example's different lengths when working with Word Embeddings (word2vec)Features of word vectors in word2vecNLP - why is “not” a stop word?What are key dataset requirements for topic models and word embeddings?What is a lower bound on the vocabulary size for generating word/sentence embedding using word2vec or skip thought vectors?use hidden layer of word2vec instead of 'one-hot', to reduce number of weights in other nets?How to replace short words into full words from tweets using pythontransfer learning with sentiment analysis?How to learn irrelevant words in an information retrieval system?
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I will be performing sentiment analysis on fiction. I'll be working with around 300 books of 350 pages. Before performing word2vec, can I limit the dictionary size by ignoring less frequent words? If so, what is the rule for defining the size?
nlp word2vec
New contributor
$endgroup$
add a comment |
$begingroup$
I will be performing sentiment analysis on fiction. I'll be working with around 300 books of 350 pages. Before performing word2vec, can I limit the dictionary size by ignoring less frequent words? If so, what is the rule for defining the size?
nlp word2vec
New contributor
$endgroup$
add a comment |
$begingroup$
I will be performing sentiment analysis on fiction. I'll be working with around 300 books of 350 pages. Before performing word2vec, can I limit the dictionary size by ignoring less frequent words? If so, what is the rule for defining the size?
nlp word2vec
New contributor
$endgroup$
I will be performing sentiment analysis on fiction. I'll be working with around 300 books of 350 pages. Before performing word2vec, can I limit the dictionary size by ignoring less frequent words? If so, what is the rule for defining the size?
nlp word2vec
nlp word2vec
New contributor
New contributor
New contributor
asked 9 mins ago
Archan LagooArchan Lagoo
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