From ece76f3eef8d30d938c0eb162f62ece4ecf888d5 Mon Sep 17 00:00:00 2001 From: Mengxuan Date: Thu, 23 Feb 2017 14:35:56 -0500 Subject: [PATCH] paraphrase --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 759f551..6bc852d 100644 --- a/README.md +++ b/README.md @@ -40,7 +40,7 @@ Sentiment Analysis happens at various levels: Most recent research focuses on the aspect-based approaches. But not all opensource implementations are caught up yet. -There are many different approaches to solve the problem. Lexical methods, for example, look at the frequency of words expressing positive and negative sentiment (from i.e. SentiWordNet) occuring in the given sentence. Supervised Machine Learning, such as Naive Bayes and Support Vector Machine (SVM.), can be used with training data. Since training examples are difficult to obtain, Unsupervised Machine Learning, such as Latent Dirichlet Allocation (LDA) and word embeddings (Word2Vec) are also used on large unlabeled datasets. Recent works also apply Deep Learning methods such as Convolutional Neural Network (CNN) and Long Short-term Memory (LSTM), as well as their attention-based variants. Those interested can find more details in the survey papers. +There are many different approaches to solve the problem. Lexical methods, for example, look at the frequency of words expressing positive and negative sentiment (from i.e. SentiWordNet) occuring in the given sentence. Supervised Machine Learning, such as Naive Bayes and Support Vector Machine (SVM.), can be used with training data. Since training examples are difficult to obtain, Unsupervised Machine Learning, such as Latent Dirichlet Allocation (LDA) and word embeddings (Word2Vec) are also used on large unlabeled datasets. Recent works also apply Deep Learning methods such as Convolutional Neural Network (CNN) and Long Short-term Memory (LSTM), as well as their attention-based variants. You will find more details in the survey papers. ## Survey Papers