![in the deep movie plot in the deep movie plot](https://cdn.i-scmp.com/sites/default/files/styles/1200x800/public/d8/images/canvas/2021/06/22/50675a3d-1afa-40ca-a281-3cdf9db9a617_eba39241.jpg)
The following script imports the required libraries: import pandas as pdįrom import one_hotįrom import pad_sequencesįrom import Activation, Dropout, Denseįrom keras.layers import GlobalMaxPooling1Dįrom import Embeddingįrom sklearn.model_selection import train_test_splitįrom import Tokenizer "positive" and "negative" which makes our problem a binary classification problem. The sentiment column can have two values i.e. The review column contains text for the review and the sentiment column contains sentiment for the review. The file contains 50,000 records and two columns: review and sentiment. If you download the dataset and extract the compressed file, you will see a CSV file. The dataset that can be downloaded from this Kaggle link. Else, you should read my previous article and then you can come back and continue with this article. It is important that you already understand these concepts. Note: This article uses Keras Embedding Layer and GloVe word embeddings to convert text to numeric form.
In the deep movie plot how to#
Furthermore, we will see how to evaluate deep learning model on a totally unseen data. We will use three different types of deep neural networks: Densely connected neural network (Basic Neural Network), Convolutional Neural Network (CNN) and Long Short Term Memory Network (LSTM), which is a variant of Recurrent Neural Networks.
![in the deep movie plot in the deep movie plot](https://m.media-amazon.com/images/M/MV5BMmQ0MWYzZjYtNGFiNS00MjE4LTliODUtMTFhOGIwMzY0ZWFmXkEyXkFqcGdeQXVyNjcwODQ2Nzc@._V1_UY1200_CR89,0,630,1200_AL_.jpg)
In this article, we will build upon the concepts that we studied in the previous article and will see classification in more detail using a real-world dataset. Finally, we only used a densely connected neural network to test our algorithm. Furthermore, the classification algorithms were trained and tested on same data. We used custom dataset that contained 16 imaginary reviews about movies. We perform basic classification task using word embeddings. The previous article was focused primarily towards word embeddings, where we saw how the word embeddings can be used to convert text to a corresponding dense vector, which can be subsequently used as input to any deep learning model. In the last article, we started our discussion about deep learning for natural language processing.
In the deep movie plot series#
This is the 17th article in my series of articles on Python for NLP.