From a1921b95c722a91db0218547810c0938955303f8 Mon Sep 17 00:00:00 2001 From: Brian Broll Date: Wed, 31 May 2017 21:17:34 -0500 Subject: [PATCH] updated html to match --- 06.understand_sentiment/index.html | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/06.understand_sentiment/index.html b/06.understand_sentiment/index.html index ffcd785..7fbd7c1 100644 --- a/06.understand_sentiment/index.html +++ b/06.understand_sentiment/index.html @@ -59,7 +59,7 @@ In natural language processing, sentiment analysis refers to determining the emo In natural language processing, sentiment analysis can be categorized as a **Text Classification problem**, i.e., to categorize a piece of text to a specific class. It involves two related tasks: text representation and classification. Before the emergence of deep learning techniques, the mainstream methods for text representation include BOW (*bag of words*) and topic modeling, while the latter contain SVM (*support vector machine*) and LR (*logistic regression*). -The BOW model does not capture all the information in a piece of text, as it ignores syntax and grammar and just treats the text as a set of words. For example, “this movie is extremely bad“ and “boring, dull, and empty work” describe very similar semantic meaning, yet their BOW representations have with little similarity. Furthermore, “the movie is bad“ and “the movie is not bad“ have high similarity with BOW features, but they express completely opposite semantics. +The BOW model does not capture all the information in a piece of text, as it ignores syntax and grammar and just treats the text as a set of words. For example, “this movie is extremely bad“ and “boring, dull, and empty work” describe very similar semantic meaning, yet their BOW representations have little similarity. Furthermore, “the movie is bad“ and “the movie is not bad“ have high similarity with BOW features, but they express completely opposite semantics. This chapter introduces a deep learning model that handles these issues in BOW. Our model embeds texts into a low-dimensional space and takes word order into consideration. It is an end-to-end framework and it has large performance improvement over traditional methods \[[1](#Reference)\]. -- GitLab