twitter-sentiment-cnn | convolutional neural network to perform sentiment | Machine Learning library

twitter-sentiment-cnn is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Tensorflow applications. twitter-sentiment-cnn has no bugs, it has no vulnerabilities and it has low support. However twitter-sentiment-cnn build file is not available. You can download it from GitLab, GitHub.

The network implemented in this script is a single layer CNN structured as follows: - Embedding layer: takes as input the tweets (as strings) and maps each word to an n-dimensional space so that it is represented as a sparse vector (see [word2vec] - Convolution layers: a set of parallel 1D convolutional layers with the given filter sizes and 128 output channels. A filter’s size is the number of embedded words that the filter covers. - Pooling layers: a set of pooling layers associated to each of the convolutional layers. - Concat layer: concatenates the output of the different pooling layers into a single tensor. - Dropout layer: performs neuron dropout (some neurons are randomly not considered during training). - Output layer: fully connected layer with a softmax activation function to perform classification.