Sentiment Analysis of Movie Reviews (2): word2vec

@machinelearnbot

This is the continuation of my mini-series on sentiment analysis of movie reviews, which originally appeared on recurrentnull.wordpress.com. Last time, we had a look at how well classical bag-of-words models worked for classification of the Stanford collection of IMDB reviews. As it turned out, the "winner" was Logistic Regression, using both unigrams and bigrams for classification. The best classification accuracy obtained was .89 So, bag-of-words models may be surprisingly successful, but they are limited in what they can do.


Sentiment Analysis of Movie Reviews (3): doc2vec

@machinelearnbot

This is the last – for now – installment of my mini-series on sentiment analysis of the Stanford collection of IMDB reviews (originally published on recurrentnull.wordpress.com). So far, we've had a look at classical bag-of-words models and word vectors (word2vec). We saw that from the classifiers used, logistic regression performed best, be it in combination with bag-of-words or word2vec. We also saw that while the word2vec model did in fact model semantic dimensions, it was less successful for classification than bag-of-words, and we explained that by the averaging of word vectors we had to perform to obtain input features on review (not word) level. So the question now is: How would distributed representations perform if we did not have to throw away information by averaging word vectors?


Sentiment Analysis of Movie Reviews (3): doc2vec

@machinelearnbot

This is the last – for now – installment of my mini-series on sentiment analysis of the Stanford collection of IMDB reviews (originally published on recurrentnull.wordpress.com). So far, we've had a look at classical bag-of-words models and word vectors (word2vec). We saw that from the classifiers used, logistic regression performed best, be it in combination with bag-of-words or word2vec. We also saw that while the word2vec model did in fact model semantic dimensions, it was less successful for classification than bag-of-words, and we explained that by the averaging of word vectors we had to perform to obtain input features on review (not word) level. So the question now is: How would distributed representations perform if we did not have to throw away information by averaging word vectors?


Sentence Similarity in Python using Doc2Vec – Kanoki

#artificialintelligence

Numeric representation of Text documents is challenging task in machine learning and there are different ways there to create the numerical features for texts such as vector representation using Bag of Words, Tf-IDF etc.I am not going in detail what are the advantages of one over the other or which is the best one to use in which case. There are lot of good reads available to explain this. It's a Model to create the word embeddings, where it takes input as a large corpus of text and produces a vector space typically of several hundred dimesions. The underlying assumption of Word2Vec is that two words sharing similar contexts also share a similar meaning and consequently a similar vector representation from the model. For instance: "Bank", "money" and "accounts" are often used in similar situations, with similar surrounding words like "dollar", "loan" or "credit", and according to Word2Vec they will therefore share a similar vector representation.


Capturing semantic meanings using deep learning

#artificialintelligence

Word embedding is an alternative technique in NLP, whereby words or phrases from the vocabulary are mapped to vectors of real numbers in a low-dimensional space relative to the vocabulary size, and the similarities between the vectors correlate with the words' semantic similarity. Many different types of models were proposed for representing words as continuous vectors, including latent semantic analysis (LSA) and latent Dirichlet allocation (LDA). In the skip-gram model, instead of using the surrounding words to predict the center word, it uses the center word to predict the surrounding words (see Figure 3). Deep Solutions delivers end-to-end software solutions based on deep learning innovative algorithms for computer vision, natural language processing, anomaly detection, recommendation systems, and more.