Text classification

• 118 Text classification using bag of words and SVM

Spec: Classify text using word frequency counts as predictors.

Language: Python (Scikit-learn), Matlab (Machine Learning Toolbox, Text Analytics Toolbox).

• 122 Sentiment detection using pretrained word embedding and SVM

Spec: Classify text by mapping words into numerical vectors..

Language: Python (Scikit-learn), Matlab (Machine Learning Toolbox, Text Analytics Toolbox).

• 121 Text classification using word embedding and LSTM

Spec: Classify text by mapping words into numerical vectors (signals).

Language: Python (Keras-Tensorflow), Matlab (Deep Learning & Neural Networks Toolbox, Text Analytics Toolbox).

• 125 Text classification using pretrained word embedding and LSTM

Spec: Classify text by mapping words into numerical vectors.

Language: Python (Keras-Tensorflow), Matlab (Deep Learning & Neural Networks Toolbox, Text Analytics Toolbox).

Text generation

• 124 Word-by-word text generation using word embedding and LSTM

Spec: Predict next word in text by mapping words into numerical vectors.

Language: Python (Keras-Tensorflow), Matlab (Deep Learning & Neural Networks Toolbox, Text Analytics Toolbox).

• 123 Character-by-character text generation using character embedding and LSTM

Spec: Predict next character in sequence of characters by mapping characters into numerical vectors.

Language: Python (Keras-Tensorflow), Matlab (Deep Learning & Neural Networks Toolbox, Text Analytics Toolbox).

• 126 Character-by-character text generation using character vocabulary and LSTM

Spec: Predict next character in sequence of characters using their indices in vocabulary.

Language: Python (Keras-Tensorflow), Matlab (Deep Learning & Neural Networks Toolbox, Text Analytics Toolbox).

Text analysis (unsupervised learning)

• 119 Text analysis using LSA(latent semantic analysis)/LDA(latent Dirichlet allocation) topic model

Spec: Cluster documents using word probabilities in topics (clusters).

Language: Python (Scikit-learn), Matlab (Text Analytics Toolbox).

• 120 Text analysis using bag of n-grams

Spec: Cluster documents using phrase (sequence of words) probabilities in topics (clusters).

Language: Python (Scikit-learn), Matlab (Text Analytics Toolbox).

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