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How to Predict Sentiment from Movie Reviews Using Deep Learning (Text Classification)

Sentiment analysis is a natural language processing problem where text is understood, and the underlying intent is predicted.

In this post, you will discover how you can predict the sentiment of movie reviews as either positive or negative in Python using the Keras deep learning library.

After reading this post, you will know:

  • About the IMDB sentiment analysis problem for natural language processing and how to load it in Keras
  • How to use word embedding in Keras for natural language problems
  • How to develop and evaluate a multi-layer perception model for the IMDB problem
  • How to develop a one-dimensional convolutional neural network model for the IMDB problem

Kick-start your project with my new book Deep Learning With Python, including step-by-step tutorials and the Python source code files for all examples.

Let’s get started.

  • Jul/2016: First published
  • Update Oct/2016: Updated for Keras 1.1.0 and TensorFlow 0.10.0
  • Update Mar/2017: Updated for Keras 2.0.2, TensorFlow 1.0.1 and Theano 0.9.0
  • Update Jul/2019: If you are using Keras 2.2.4 and NumPy 1.16.2+ and get “ValueError: Object arrays cannot be loaded when allow_pickle=False“, then try updating NumPy to 1.16.1, update Keras to the version from github, or use the fix described here
  • Update Sep/2019: Updated for Keras 2.2.5
  • Update Jul/2022: Updated for TensorFlow 2.x API
Predict Sentiment From Movie Reviews Using Deep Learning

Predict sentiment from movie reviews using deep learning
Photo by SparkCBC, some rights reserved.

IMDB Movie Review Sentiment Problem Description

The dataset is the Large Movie Review Dataset, often referred to as the IMDB dataset.

The IMDB dataset contains 25,000 highly polar movie reviews (good or bad) for training and the same amount again for testing. The problem is to determine whether a given movie review has a positive or negative sentiment.

The data was collected by Stanford researchers and used in a 2011 paper [PDF] where a split of 50/50 of the data was used for training and test. An accuracy of 88.89% was achieved.

The data was also used as the basis for a Kaggle competition titled “Bag of Words Meets Bags of Popcorn” from late 2014 to early 2015. Accuracy was achieved above 97%, with winners achieving 99%.

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Load the IMDB Dataset with Keras

Keras provides built-in access to the IMDB dataset.

The allows you to load the dataset in a format that is ready for use in neural networks and deep learning models.

The words have been replaced by integers that indicate the absolute popularity of the word in the dataset. The sentences in each review are therefore comprised of a sequence of integers.

Calling imdb.load_data() the first time will download the IMDB dataset to your computer and store it in your home directory under ~/.keras/datasets/imdb.pkl as a 32-megabyte file.

Usefully, the imdb.load_data() provides additional arguments, including the number of top words to load (where words with a lower integer are marked as zero in the returned data), the number of top words to skip (to avoid the repeated use of “the”), and the maximum length of reviews to support.

Let’s load the dataset and calculate some properties of it. You will start by loading some libraries and the entire IMDB dataset as a training dataset.

Next, you can display the shape of the training dataset.

Running this snippet, you can see that there are 50,000 records.

You can also print the unique class values.

You can see it is a binary classification problem for good and bad sentiment in the review.

Next, you can get an idea of the total number of unique words in the dataset.

Interestingly, you can see that there are just under 100,000 words across the entire dataset.

Finally, you can get an idea of the average review length.

You can see that the average review has just under 300 words with a standard deviation of just over 200 words.

Looking at a box and whisker plot for the review lengths in words, you can see an exponential distribution that you can probably cover the mass of the distribution with a clipped length of 400 to 500 words.

Review Length in Words for IMDB Dataset

Review length in words for IMDB dataset

Word Embeddings

A recent breakthrough in the field of natural language processing is called word embedding.

This technique is where words are encoded as real-valued vectors in a high-dimensional space, where the similarity between words in terms of meaning translates to closeness in the vector space.

Discrete words are mapped to vectors of continuous numbers. This is useful when working with natural language problems with neural networks and deep learning models as they require numbers as input.

Keras provides a convenient way to convert positive integer representations of words into a word embedding by an Embedding layer.

The layer takes arguments that define the mapping, including the maximum number of expected words, also called the vocabulary size (e.g., the largest integer value that will be seen as an integer). The layer also allows you to specify the dimensionality for each word vector, called the output dimension.

You want to use a word embedding representation for the IMDB dataset.

Let’s say that you are only interested in the first 5,000 most used words in the dataset. Therefore, your vocabulary size will be 5,000. You can choose to use a 32-dimension vector to represent each word. Finally, you may choose to cap the maximum review length at 500 words, truncating reviews longer than that and padding reviews shorter than that with 0 values.

You will load the IMDB dataset as follows:

You will then use the Keras utility to truncate or pad the dataset to a length of 500 for each observation using the sequence.pad_sequences() function.

Finally, later on, the first layer of your model would be a word embedding layer created using the Embedding class as follows:

The output of this first layer would be a matrix with the size 32×500 for a given review training or test pattern in integer format.

Now that you know how to load the IMDB dataset in Keras and how to use a word embedding representation for it, let’s develop and evaluate some models.

Simple Multi-Layer Perceptron Model for the IMDB Dataset

You can start by developing a simple multi-layer perceptron model with a single hidden layer.

The word embedding representation is a true innovation, and you will demonstrate what would have been considered world-class results in 2011 with a relatively simple neural network.

Let’s start by importing the classes and functions required for this model and initializing the random number generator to a constant value to ensure you can easily reproduce the results.

Next, you will load the IMDB dataset. You will simplify the dataset as discussed during the section on word embeddings—only the top 5,000 words will be loaded.

You will also use a 50/50 split of the dataset into training and test sets. This is a good standard split methodology.

You will bound reviews at 500 words, truncating longer reviews and zero-padding shorter ones.

Now, you can create your model. You will use an Embedding layer as the input layer, setting the vocabulary to 5,000, the word vector size to 32 dimensions, and the input_length to 500. The output of this first layer will be a 32×500-sized matrix, as discussed in the previous section.

You will flatten the Embedded layers’ output to one dimension, then use one dense hidden layer of 250 units with a rectifier activation function. The output layer has one neuron and will use a sigmoid activation to output values of 0 and 1 as predictions.

The model uses logarithmic loss and is optimized using the efficient ADAM optimization procedure.

You can fit the model and use the test set as validation while training. This model overfits very quickly, so you will use very few training epochs, in this case, just 2.

There is a lot of data, so you will use a batch size of 128. After the model is trained, you will evaluate its accuracy on the test dataset.

Tying all of this together, the complete code listing is provided below.

Running this example fits the model and summarizes the estimated performance.

Note: Your results may vary given the stochastic nature of the algorithm or evaluation procedure, or differences in numerical precision. Consider running the example a few times and compare the average outcome.

You can see that this very simple model achieves a score of 87%, which is in the neighborhood of the original paper, with minimal effort.

You can likely do better if you trained this network, perhaps using a larger embedding and adding more hidden layers.

Let’s try a different network type.

One-Dimensional Convolutional Neural Network Model for the IMDB Dataset

Convolutional neural networks were designed to honor the spatial structure in image data while being robust to the position and orientation of learned objects in the scene.

This same principle can be used on sequences, such as the one-dimensional sequence of words in a movie review. The same properties that make the CNN model attractive for learning to recognize objects in images can help to learn structure in paragraphs of words, namely the techniques invariance to the specific position of features.

Keras supports one-dimensional convolutions and pooling by the Conv1D and MaxPooling1D classes, respectively.

Again, let’s import the classes and functions needed for this example and initialize your random number generator to a constant value so that you can easily reproduce the results.

You can also load and prepare the IMDB dataset as you did before.

You can now define your convolutional neural network model. This time, after the Embedding input layer, insert a Conv1D layer. This convolutional layer has 32 feature maps and reads embedded word representations’ three vector elements of the word embedding at a time.

The convolutional layer is followed by a 1D max pooling layer with a length and stride of 2 that halves the size of the feature maps from the convolutional layer. The rest of the network is the same as the neural network above.

You will also fit the network the same as before.

Tying all of this together, the complete code listing is provided below.

Running the example, you are first presented with a summary of the network structure. You can see your convolutional layer preserves the dimensionality of your Embedding input layer of 32-dimensional input with a maximum of 500 words. The pooling layer compresses this representation by halving it.

Note: Your results may vary given the stochastic nature of the algorithm or evaluation procedure, or differences in numerical precision. Consider running the example a few times and compare the average outcome.

Running the example offers a slight but welcome improvement over the neural network model above with an accuracy of 87%.

Again, there is a lot of opportunity for further optimization, such as using deeper and/or larger convolutional layers.

One interesting idea is to set the max pooling layer to use an input length of 500. This would compress each feature map to a single 32-length vector and may boost performance.


In this post, you discovered the IMDB sentiment analysis dataset for natural language processing.

You learned how to develop deep learning models for sentiment analysis, including:

  • How to load and review the IMDB dataset within Keras
  • How to develop a large neural network model for sentiment analysis
  • How to develop a one-dimensional convolutional neural network model for sentiment analysis

Do you have any questions about sentiment analysis or this post? Ask your questions in the comments, and I will do my best to answer.

171 Responses to How to Predict Sentiment from Movie Reviews Using Deep Learning (Text Classification)

  1. Avatar
    Vishal September 12, 2016 at 1:29 am #

    imdb.load_data(nb_words=5000, test_split=0.33)

    TypeError: load_data() got an unexpected keyword argument ‘test_split’

    The test_split argument doesn’t appear to exist in Keras 1.08, perhaps I’m doing something wrong?

    • Avatar
      Jason Brownlee September 12, 2016 at 8:33 am #

      The API has changed, sorry. I will update the example. You can remove the “test_split” argument.

      • Avatar
        Jason Brownlee October 7, 2016 at 2:06 pm #

        I have updated the example to match the API changes for Keras 1.1.0 and TensorFlow 0.10.0.

      • Avatar
        Jason June 29, 2018 at 11:48 pm #

        hi , thanks for your tutorial. but I’m wondering if you have any tutorial about Aspect-based sentiment analysis

        • Avatar
          Jason Brownlee June 30, 2018 at 6:08 am #

          What is aspect-based sentiment analysis?

          • Avatar
            Joshua July 31, 2018 at 9:55 pm #

            A multi-class classification problem where each sentence is associated to an ‘aspect’. There are two forms; categorical and term-based. Datasets available:


            Two papers worth reviewing:
            Deep Learning for Aspect-Based Sentiment Analysis By Bo Wang and Min Liu
            Aspect-Based Sentiment Analysis Using a Two-Step Neural Network Architecture by S Jebbara and P Cimiano.

            There are unsupervised versions of it (term extraction) but categorical is probably more desirable. This dataset would need to be labeled.

          • Avatar
            Jason Brownlee August 1, 2018 at 7:43 am #

            Thanks for sharing.

  2. Avatar
    Joe Williams October 3, 2016 at 12:52 am #

    Hi, Jason,

    Thanks for the great tutorial! How could I modify this to perform sentiment analysis based on user input? Or from a Twitter stream?

    Best wishes

    • Avatar
      Jason Brownlee October 3, 2016 at 5:19 am #

      You would have to encode the tweets using the same mapping from characters to integers.

      I do not have code for you to do this at the moment.

  3. Avatar
    Jie November 9, 2016 at 4:15 pm #

    1. embeddings are trainable, right? I mean, embeddings are dynamic, and they are changing during the training?

    2. How to save the embeddings to a file? We have to load the embeddings to predict new data in the future.

    3. I have a file named I know how to load the model and graph architecture from here:
    But how to load embeddings in in order to predict new data?

    • Avatar
      Jason Brownlee November 10, 2016 at 7:38 am #

      Hi Jie, great questions.

      An embedding is a projection, they can be prepared from data. I would not say they are learned, but perhaps you could say that.

      I believe they can be prepared deterministically so we would not have to save them to file. I could be wrong about that, but that is my intuition.

  4. Avatar
    Maxim December 19, 2016 at 5:23 am #

    Jason, many thanks for your lessons! They’re amazing!!!!

    Maybe I ask you a very stupid question, but I can’t understand one thing. What is the Embedding layer? Could you show an example of it. I mean this is word vector with dimentionality of 500X32. So how it looks like?

    [0,0,1,….0,0,0] X 32
    [0,1,0,….0,0,0] X 32

    What digits in it? And why if we lift it dimensionality up to 64, the accuracy will rise up?

    Thank you!

  5. Avatar
    Martin December 27, 2016 at 7:27 am #

    test_split = 0.33

    This is defined, but not used anywhere in the code. why is that?

    • Avatar
      Jason Brownlee December 28, 2016 at 7:01 am #

      It is a typo and should be removed, thanks Martin. I will update the example soon.

  6. Avatar
    Anand December 28, 2016 at 7:16 pm #

    What params would you change typically to achieve what you have mentioned towards the end of the article to improve accuracy?

    To quote you: “set the max pooling layer to use an input length of 500. This would compress each feature map to a single 32 length vector”

    Could you please help what params (which lines) would need this change?

    • Avatar
      Jason Brownlee December 29, 2016 at 7:15 am #

      Hi Anand, use some trial and error and tune the method for the problem.

  7. Avatar
    Jim January 13, 2017 at 1:38 am #

    I am testing the predicted probabilities and values using model.predict(X_test) and model.predict_classes(X_test)

    I noticed that the predicted probabilities for the 0 class are all 0.5 w/ median predicted probability of 0.9673.

    Am I correct in assuming the model.predict returns probability of the 1 class always, and predicts a class of 0 when that probability is < 50%?

  8. Avatar
    brent January 22, 2017 at 4:59 am #

    When an element in the data is labeled as an integer, let’s say 4 for example, could that represent any word that has occurred 4 times in the data or is it a representation of a unique word?

    • Avatar
      Jason Brownlee January 22, 2017 at 5:13 am #

      Hi brent, each integer represents a unique word.

      • Avatar
        GT May 24, 2017 at 7:34 pm #

        Hi Jason,

        Thanks for the great tutorial, great as usual!

        You mentioned that “each integer represents a unique word”, why?

        My assumption is that we have mapped each word to its frequency in the whole corpus. If my assumption is true, so two words could come up with the same frequency. For example “Dog” and “Cat” both could repeat 10 times in the corpus.

        Could you please if my assumption is wrong?


        • Avatar
          Jason Brownlee June 2, 2017 at 11:33 am #

          Words were ordered by frequency then assigned integers based on that frequency.

  9. Avatar
    Agustin February 3, 2017 at 9:15 am #

    Hello Jason, recently i became acquainted with the basics in machine learning and deep learning, in part thanks to the information provided in this site, which I found most insightful.
    However, lately I came upon a problem of generation simple and short questions automatically from a text. Due to my lack of knowledge and expertise i cant asses if it is possible to solve this problem with Deep Learning or any other method. Currently I have a database of several thousand questions based on around a hundred corpora that could be used as training data. Do you think I could get any successful results, and if so what approach will be the best? (Consider that even if it makes gibberish 50% of the time, it will still save a lot of work)

    • Avatar
      Jason Brownlee February 3, 2017 at 10:19 am #

      It does sound like a good deep learning problem Agustin.

      I’d recommend reading some papers on the topic to spark some ideas on methods and ways to represent the problem.

      Try searching on google scholar and on arvix.

  10. Avatar
    Chris Shumaker February 9, 2017 at 7:15 am #

    Hi, thank you for the example! Do you know the NumPy and matplotlib versions you were using in this example? I am having trouble with several methods like mean, std and box plot.

    • Avatar
      Jason Brownlee February 9, 2017 at 7:28 am #

      Hi Chris,

      This might be a Python 2 vs Python 3 issue, I used Python 2.

    • Avatar
      Chris Shumaker February 9, 2017 at 7:34 am #

      Actually, I am thinking that it is the call to map(). What version of Python are you using?

      • Avatar
        Chris Shumaker February 9, 2017 at 7:36 am #

        Sorry for post-spam. This works in Python 3:

        # Summarize review length
        print(“Review length: “)
        result = list(map(len, X))

        Python 3.5.2 :: Anaconda 4.1.1 (x86_64)
        Keras (1.2.1)
        tensorflow (0.12.1)
        numpy (1.11.2)
        matplotlib (1.5.3)

  11. Avatar
    Akshit Bhatia February 12, 2017 at 6:44 am #

    Can you give me some idea on how to implement other Deep Learning techniqeus such as recursive autoencoders(RAE) , RBM deep learning algorithm for sentiment analysis
    Any help would be appreciated :).


    • Avatar
      Jason Brownlee February 13, 2017 at 9:08 am #

      Hi Akshit,

      I don’t have an example of RAE or RBM.

      This post has an example of sentiment analysis that you can use as a starting point.

  12. Avatar
    Zhang February 12, 2017 at 8:47 am #

    Hello, thanks for the example. I really appreciate you if you suggest me why I got this error.

    File “C:\Anaconda\lib\site-packages\theano-0.9.0.dev4-py2.7.egg\theano\gof\”, line 2236, in compile_str
    raise MissingGXX(“g++ not available! We can’t compile c code.”)

    MissingGXX: (‘The following error happened while compiling the node’, Shape_i{1}(embedding_2_W), ‘\n’, “g++ not available! We can’t compile c code.”, ‘[Shape_i{1}(embedding_2_W)]’)

    • Avatar
      Chri February 12, 2017 at 2:51 pm #

      @Zhang, looks like you have a beta version of Theano. If you’re just looking to get started, maybe you want to try a stable channel instead? Looks like your error is because you’re installing from source and your environment isn’t set up quite right.

    • Avatar
      Jason Brownlee February 13, 2017 at 9:09 am #

      Hi Zhang,

      It looks like g++ is not available. I’m not a windows user, I’m not sure how to interpret this message.

      Consider searching or posting on stack overflow.

  13. Avatar
    Chris February 12, 2017 at 2:55 pm #

    @Jason, thanks for your reply and thanks again for the post!

    I am having trouble improving the results on this model. I have changed the pool_length (500,250,125,375,5,10,15,20), tried adding another dense layer at size 250 and 500, and changed the number of epochs (25,50).

    Do you have any recommendations for tweaking the model? I tried the suggestions (deeper, larger, pool_length, and also number of epochs). Do you have any tips or reading suggestions for improving performance in general? This seems to be my last ‘wall’ to really being able to do ML.


  14. Avatar
    Kiran March 3, 2017 at 4:34 pm #

    Hi jason, I removed the denser layer with 250 neurons and it reduced the number of parameters to be trained drastically with an increased accuracy of about 1% over 5 epochs. Any idea why you added 2 dense layers after flatten layer?

    • Avatar
      Jason Brownlee March 6, 2017 at 10:42 am #

      Well done Kiran.

      I came up with the configuration after some brief trial and error. It was not optimized.

  15. Avatar
    Dan March 12, 2017 at 12:02 am #

    Does it make sense to specify validation_data as X_test,y_test in the fit function if we evaluate our model in the scores function afterwards? Or could we skip specify validation_data in…)?

    • Avatar
      Jason Brownlee March 12, 2017 at 8:28 am #

      No, you will get this for free when fitting your model. The validation data should be different from training data and is completely optional.

  16. Avatar
    Harish March 23, 2017 at 5:54 pm #

    What is the accuracy of this approach? Which approach is better to get accuracy of at least 0.89?

  17. Avatar
    Prakash April 12, 2017 at 8:23 am #

    I tried the sentiment analysis with convolutional NNs and LSTMs and find the CNNs give higher accuracy. Any insight into why?

    • Avatar
      Jason Brownlee April 12, 2017 at 9:35 am #

      Some ideas:

      Perhaps the CNNs are better at capturing the spatial relationships.

      Perhaps the LSTM needs to be larger and trained for longer to achieve the same skill.

  18. Avatar
    Patrick April 17, 2017 at 9:20 pm #

    Please add these to the imports

    from keras.preprocessing import sequence
    from keras.layers.embeddings import Embedding

    • Avatar
      Jason Brownlee April 18, 2017 at 8:32 am #

      These are listed in the imports for the section titled “One-Dimensional Convolutional Neural Network Model for the IMDB Dataset”

  19. Avatar
    Trang April 27, 2017 at 2:21 pm #

    Hi, Jason, i have the same question with Maxim. Can you tell me why is that.Thank you
    Maybe I ask you a very stupid question, but I can’t understand one thing. What is the Embedding layer? Could you show an example of it. I mean this is word vector with dimentionality of 500X32. So how it looks like?

    [0,0,1,….0,0,0] X 32
    [0,1,0,….0,0,0] X 32

    What digits in it? And why if we lift it dimensionality up to 64, the accuracy will rise up?

    Thank you!

  20. Avatar
    Ahmed May 10, 2017 at 3:19 am #

    Thanks So So Much For This Awesome Tutorial .
    However , I’m Asking About How To Use This To Predict The Opinion
    I Still Don’t know , for instance i need to know the movie is good or bad , or if i used a twitter data set i need to know the public opinion summary about a specific hashtag or topic

    I tried More And More But i Failed as i’m still Beginner

    Thanks In Advance <3

  21. Avatar
    Hamza May 24, 2017 at 5:30 am #

    Well, thank you so much for this great work.

    I have a question here. I didn’t understand why we use ReLU instead of tanh as the activation function. Most people use SGD or backpropagation for training. What did we use here? I do not know about ADAM. Can you please explain why did you use it for training?

    • Avatar
      Jason Brownlee June 2, 2017 at 11:28 am #

      ReLU has better properties than sigmoid or tanh and has become the new defacto standard.

      We did use SGD to fit the model, we just used a more fancy version called Adam.

  22. Avatar
    Adnan June 12, 2017 at 2:49 am #

    Hi, thanks a lot for this HELPFUL tutorial. I have a question, could be an odd one. what if we use pre-trained word2vec model. I mean if we just use pre-trained word2vec model and train our neural network with movie reviews data. Correct me if I am wrong!

    Or best way is to train word2vec with movie reviews data, then train neural network with same movie reviews data then try.

    Kindly guide me. Thanks

    • Avatar
      Jason Brownlee June 12, 2017 at 7:11 am #

      Sounds great, try it.

      • Avatar
        Adnan June 12, 2017 at 7:53 pm #

        but should I use pre-trained word2vec model (trained with wiki data) or train from scratch with by using movie reviews data or amazon product reviews data. thanks

  23. Avatar
    Alok June 22, 2017 at 12:42 am #

    Hi Jason,

    I am trying to use tfidf matrix as input to my cnn for document classification. I don’t want to use embedding layer. Can you help me how this can be achieved. I have not seen any example which shows tfidf as input to Cov1d layer in Keras. Please help

  24. Avatar
    Matteo July 7, 2017 at 5:50 pm #

    Dear Jason, I do thank you for this great post. Could you give me any indications on how to extend this approach to multiclass classification?

    • Avatar
      Jason Brownlee July 9, 2017 at 10:38 am #

      Set the number of nodes in the output layer to the number of classes and change activation to softmax.

  25. Avatar
    Prakash July 12, 2017 at 2:52 am #

    I see that you have not used any regularizers in the model. How is overfitting avoided?

    • Avatar
      Jason Brownlee July 12, 2017 at 9:50 am #

      Here, by an underspecified model and under-training the model.

      You could also try dropout and weight regularization.

  26. Avatar
    Rahul July 22, 2017 at 4:53 am #

    Hey Jason great review but I was wondering how I could use the created model to predict the sentiment of a new inputted text.

    • Avatar
      Jason Brownlee July 22, 2017 at 8:39 am #

      You can encode your test using the same method as in the problem in order to make a prediction.

      I hope to have many more NLP examples soon.

  27. Avatar
    mm August 23, 2017 at 5:45 pm #

    5000 means ?

    • Avatar
      Jason Brownlee August 24, 2017 at 6:27 am #

      That is the number of samples in the train and test sets.

  28. Avatar
    Dev September 13, 2017 at 8:03 pm #

    I am getting an error: name ‘sequence’ is not defined in the line: X_train = sequence.pad_sequences(X_train, maxlen=500)

    • Avatar
      Jason Brownlee September 15, 2017 at 12:04 pm #

      Ensure you copy all of the code, including this line:

  29. Avatar
    Hamada Zahera September 21, 2017 at 6:55 pm #

    Hi Jason,

    Thanks for this nice post . I have a question about how to use the model to predict sentiment of a new text .

    myText=”Hello, this is a my review”

    How to structure this text into model ? and use predict function.

    • Avatar
      Jason Brownlee September 22, 2017 at 5:37 am #

      I have a suite of posts on this topic scheduled for the coming weeks if you can hang on?

      • Avatar
        Dimuthu de Silva September 23, 2017 at 2:54 pm #

        I am stuck with making new predictions too..

  30. Avatar
    Parth October 5, 2017 at 1:34 am #

    Hi Jason,

    Thanks for the blog and tutorial. Since you talked about IMDB standard dataset. I just wrote a blog mentioning the accuracies of state-of-the-art models for sentiment analysis on IMDB dataset. You can see it here:

  31. Avatar
    Asad October 7, 2017 at 4:49 pm #

    Hi jason,
    you did a great work. i appreciate your effort. Actually i just want to know,in “One-Dimensional Convolutional Neural Network Model for the IMDB Dataset”. how i can know the answer of these question.
    1)number of neurons used in this tutorial?
    2)no of hidden layers?
    3)no of out put layers?
    4)if no of hidden layers maximize and minimize then output will affect or not?
    5)how many parameters is used?
    6)how we can draw a network diagram in python using code or library?

    please answer me briefly. thanks

    • Avatar
      Jason Brownlee October 8, 2017 at 8:28 am #

      We cannot know for sure, these configuration hyperparameters cannot be specified analytically. You must use experiments to discover what works best for a given problem.

      You can draw a network diagram in Keras, here are the API details:

      • Avatar
        Asad October 10, 2017 at 8:36 pm #

        how i can do experiments? on which bases i can do experiments? do you have any tutorial in which you take a dataset manually in any NN for sentiment analysis?

        • Avatar
          Jason Brownlee October 11, 2017 at 7:51 am #

          Yes, I have a few posts scheduled for later this month.

  32. Avatar
    Asad October 7, 2017 at 4:57 pm #

    hi jason

    i want to know that how i can use this model for my own data set which is in csv file. the data set have polarity of idiom sentences.

    • Avatar
      Jason Brownlee October 8, 2017 at 8:29 am #

      I will have an example on the blog soon.

      • Avatar
        Asad October 9, 2017 at 3:30 pm #

        ok jason i am waiting.. please also send notification to me. because on your blog there is no notification or email recieved about any answer.

  33. Avatar
    Yang Cheng November 1, 2017 at 2:13 pm #

    Hi Jason, great post! I have learnt a lot from your previous posts. Thanks a lot!
    For sentiment analysis, we can get better result (0.8822 in this case) if we change the output layer to softmax activation.

  34. Avatar
    Sai Rohit S November 14, 2017 at 5:36 pm #

    Hi Jason.
    I have used a CNN Model for Sentiment Analysis.
    I have a set of my own strings to test the model on.
    What is the conversion I need to make on my Strings to input them to the model?

  35. Avatar
    Sai Rohit S November 15, 2017 at 2:17 pm #

    Thank You.
    I will go through it.

  36. Avatar
    Amit Adesara November 23, 2017 at 5:44 pm #

    Hi, Does Conv2D give better accuracy. What will be the input shape in that case. Have your covered Conv2D in your book on Deep Learning for NLP.


    • Avatar
      Jason Brownlee November 24, 2017 at 9:36 am #

      It is not a question of accuracy, rather what is appropriate for the data.

      1D data like a sequence of words requires a 1D convolutional neural net.

      • Avatar
        Amit Adesara November 29, 2017 at 6:54 pm #

        Hi Jason, in one of the examples in your book on NLP (chapter on text classification) the model.predict requires three input arrays as we are working with 3 channels. Though model.evaluate runs fine i fail to understand where do we define the three input in predict.sentiment function.

        Will be helpful if you can guide.

        • Avatar
          Jason Brownlee November 30, 2017 at 8:08 am #

          You can pass 3 inputs to the predict function as an array:

          Does that help?

  37. Avatar
    Abdur Rehman Nadeem December 14, 2017 at 5:40 am #

    Hi Jason,

    You used pre-built dataset but if I want to run this model on my dataset (e.g. in my case i want to run this on my tweets dataset) how can I made my dataset compatible to this blog code. Please give some suggestions or reference so that I can make tweets dataset accordant to this model.

    Best Regards,

  38. Avatar
    Ali January 28, 2018 at 4:09 pm #

    Thanks Jason,
    I learned a lot from your posts.
    This model performs well for binary classification but poorly on multiclass. My question is, in case of multiclass (say 10 or more classes), what changes in layers or their parameters will improve the accuracy?

    • Avatar
      Jason Brownlee January 29, 2018 at 8:15 am #

      I would recommend trying a suite of configurations to see what works best on your specific data.

  39. Avatar
    shweta khairnar February 21, 2018 at 7:56 am #

    hey jason, hi great tutorial but i don;t know why i am getting an error in pad.sequences line it is saying name sequnce is not defined i do not understand why i am getting this error? can you help?

    • Avatar
      Jason Brownlee February 22, 2018 at 11:10 am #

      Sorry to hear that. Are you able to check that you copied all of the code?

  40. Avatar
    bhavik February 23, 2018 at 5:15 pm #

    Hey thanks for the great work, just wanted to know that how to print out the results of this prediction as what are the positive and negative comments?

  41. Avatar
    lana February 28, 2018 at 10:02 pm #

    i also have this mistake: NameError Traceback (most recent call last)
    in ()
    —-> 1 X_train = sequence.pad_sequences(X_train, maxlen=500)
    2 X_test = sequence.pad_sequences(X_test, maxlen=500)

    NameError: name ‘sequence’ is not defined

  42. Avatar
    Svetlana Bondareva March 5, 2018 at 8:15 am #

    hey Jason, how can I use “predict” with the model? for example, I have a text and I want to see the outcome based on the model. TIA

    • Avatar
      Jason Brownlee March 6, 2018 at 6:07 am #

      You can use model.predict() with new data as an argument.

      Note that new data will need to be prepared in the same way as the training data.

  43. Avatar
    aima othman March 5, 2018 at 6:44 pm #

    can you share how to do a prediction with the model?

    • Avatar
      Jason Brownlee March 6, 2018 at 6:11 am #


  44. Avatar
    Uchenna Iheanacho March 10, 2018 at 6:56 pm #

    Hey Jason,
    How do I classify the user as angry or not angry based on the type of words in the review sent?

    • Avatar
      Jason Brownlee March 11, 2018 at 6:22 am #

      Start by collecting examples of angry and not angry movie reviews.

  45. Avatar
    mariya March 25, 2018 at 3:51 am #

    hi Mr jason … can u tell howa how can i do the file test of this model … and thanks !

  46. Avatar
    PForet March 28, 2018 at 4:27 am #

    Very nice article! Clear and precise. I just have some reserves concerning the use of deep learning for sentiment classification. Sure, it seems to be the future somehow, but for now, we just get better results with Bayesian methods. For instance, I worked with the same dataset (see and get 91.6% accuracy with Bayesian learning. Do you think deep learning performs less because the task is too simple? Or because the dataset is too small?
    I’d love to hear your thoughts about that

  47. Avatar
    Jane_in May 30, 2018 at 11:36 pm #

    Hye Jason. I would like to tell that as my first attempt I tried multi-layerd perceptron and i have this issue that is it okay to use the same data for validating as well a testing? if we will validate our training on test data then the result will be biased??
    And whenever I am increasing no. of epochs loss is increasing simultaneously and accuracy is just around 86% always. Please guide me.

  48. Avatar
    Ram June 4, 2018 at 4:13 pm #

    Hi Jason!!!
    what will be x_train,x_test,y_train and y_test in case of twitter sentiments
    where labels are floating point values?
    I am not getting what are these lists?
    can you clear it?

    • Avatar
      Jason Brownlee June 5, 2018 at 6:34 am #

      Inputs will be text, output will be a sentiment class label.

      Train and test will be some split of the data for fitting and evaluating the model respectively.

  49. Avatar
    Jane_in June 4, 2018 at 8:29 pm #

    I want to know the reason for increasing validation loss after 2nd epoch..?? is there any mistake i am doing?

  50. Avatar
    Bharath June 8, 2018 at 7:50 pm #

    Thank you Jason for this!

    I am really new to deep learning and NLP.

    Here are few naive questions that I have:
    – Why did you select 32 as the parameter?
    – Can we use this learned model to now predict any other text data? say for eg, i wanted to evaluate feedback data of customers, can i use the same model to do so? If so, how?
    – How are you taking care of stop words and other irrelevant terms in this text?

    Thanks in advance 🙂

  51. Avatar
    Kaushal June 11, 2018 at 4:33 pm #

    Hi Jason,
    What is the current benchmark accuracy for imdb movie review classification?

  52. Avatar
    Bharath Nair June 12, 2018 at 12:45 am #

    thank you Jason for the prompt response 🙂 Really appreciate it.

  53. Avatar
    Ritwik June 12, 2018 at 5:22 am #

    Hey Jason!

    excellent stuff. How to use your code to predict the same corpus and model to predict employee feedback information? I do not understand how to use this on some other data. How to use your code as starting point?

  54. Avatar
    SOORAJ June 21, 2018 at 10:28 pm #

    Your tutorials are very helpful as a beginner like me. I have a doubt if we are using a dataset having only two labels (class & text) and then how many input neurons should I create. Is it 1 or more than one..??

  55. Avatar
    SOORAJ June 25, 2018 at 5:25 pm #

    What is the use of Flatten()?

    • Avatar
      Jason Brownlee June 26, 2018 at 6:34 am #

      In some models, the network will have a 2d or 3d internal shape to the data. Flatten squashes this down to 1D as the fully-connected layers expect.

      • Avatar
        SOORAJ June 26, 2018 at 10:05 pm #

        thank you…..

  56. Avatar
    Ishay Telavivi August 14, 2018 at 6:04 am #

    Hi Jason,

    Thanks so much. Great post!

    I have the followng questions:
    1. You explained well why maxlen was set to 500. Is there a way to examine other lengths is an easy way (like we grid search over the model hyperparameters), and not manually? Is it important?

    2. On the “One Dimensional CNN” section, you used pool_size=2 within the maxpool function. What is reason/benefit for that?

    3. Is the flatten layer right after it is because the pool_size is 2? I mean – I won’t be needing it if I just take the default?

    • Avatar
      Jason Brownlee August 14, 2018 at 6:26 am #

      You can experiment with other lengths.

      Max pool of 2 reduces he size of the filter maps to 1/4 their size. It is a commonly used configuration.

      Flatten is to reduce the filter map structure down to a vector that the dense layer can take as input.

  57. Avatar
    Ishay Telavivi August 15, 2018 at 12:28 am #

    OK Thank you

  58. Avatar
    Bahara October 1, 2018 at 8:31 pm #

    I have a question, assuming that i want to inject some hand-crafted features into CNN layers for sentiment analysis.
    At first i want to know is it possible?
    And then how can i use fully connected layer to do this?? I dont want to use any methods like svm or … just want to use combinitation of deep and hand-crafted features directly in cnn. Thank you

  59. Avatar
    PIYUSH KILLA November 25, 2018 at 12:43 am #

    how can I predict the result for my new comment?
    ex = “this movies is fantastic”

  60. Avatar
    Belgacem BRAHIMI February 25, 2019 at 4:41 am #

    thanks for this tutorial

    I have a question about how to use cross- validation ?

  61. Avatar
    Mithu March 18, 2019 at 12:01 am #

    Hi, how do you modify the learning rate?

  62. Avatar
    Chan Kim March 22, 2019 at 1:26 pm #

    Hi, Jason, thank you again for these kind tutorials. I’m learning many things here.
    In the CNN case, I see from the model summary that the number of parameters in the first Embedding layer is 160000 (5000*32) and I can understand this. But why is the number of parameters of the first Conv1D layer 3104? I guess the 500 input words are transformed to 500 * 32 output values in the Embedding layer and the convolution is done for this 500*32 inputs values with kernel size 3. Is my understanding correct?
    And I found the only way I can make 3104 seems to be 3*32*32+32 but I cannot think of any way the 1D conv parameters are applied for the input values. Could you elaborate on 1-D convolution in this case? (I read but the explanation is confusing to me..)

    • Avatar
      Jason Brownlee March 22, 2019 at 2:36 pm #

      A good starting point is to summarize the model and look at the output shape of each layer.

  63. Avatar
    Chan Kim March 22, 2019 at 1:35 pm #

    Hi, Jason, this is a follow-up question to my previous question.
    I thought 3104 = (32+1) * (3*32) so there are 3 kernel params for each 32 values for the inputs, and these kernel values are each different on each 32 inputs(3*32) and all the 32 inputs values are used to make each one of 32 output values with bias(thus * (32+1)). Hope you could understand what I mean.. 🙂

  64. Avatar
    Royal June 29, 2019 at 8:17 pm #

    Hi Jason,
    I’m getting an error with the command (X_train, y_train), (X_test, y_test) = imdb.load_data()
    using Python 3.6.8 [Anaconda], win32 version on Windows10:

    Traceback (most recent call last):
    File “”, line 2, in
    File “C:\Users\XXX\AppData\Local\Continuum\anaconda3\envs\env_python_3.6\lib\site-packages\keras\datasets\”, line 59, in load_data
    x_train, labels_train = f[‘x_train’], f[‘y_train’]
    File “C:\Users\XXX\AppData\Local\Continuum\anaconda3\envs\env_python_3.6\lib\site-packages\numpy\lib\”, line 262, in __getitem__
    File “C:\Users\XXX\AppData\Local\Continuum\anaconda3\envs\env_python_3.6\lib\site-packages\numpy\lib\”, line 692, in read_array
    raise ValueError(“Object arrays cannot be loaded when ”
    ValueError: Object arrays cannot be loaded when allow_pickle=False

    • Avatar
      Jason Brownlee June 30, 2019 at 9:39 am #

      I’m sorry to hear that, perhaps try updating NumPy?

  65. Avatar
    Royal June 30, 2019 at 5:04 pm #

    Ok, I upgraded from ‘1.16.3’ –> ‘1.16.4’ and obtained the same error.
    Does (X_train, y_train), (X_test, y_test) = imdb.load_data() still work for you?
    This is an interesting tutorial, would be a pity if it could no longer be used1

  66. Avatar
    Royal July 4, 2019 at 7:44 pm #

    Brilliant, thanks, that brings us forward! Before uncorking the champagne bottle, two more incompatibilities show up next:

    print(“Mean %.2f words (%f)” % (numpy.mean(result), numpy.std(result)))

    Traceback (most recent call last):
    File “”, line 1, in
    File “C:\Users\XXX\AppData\Local\Continuum\anaconda3\envs\env_python_3.6\lib\site-packages\numpy\core\”, line 3118, in mean out=out, **kwargs)
    File “C:\Users\XXX\AppData\Local\Continuum\anaconda3\envs\env_python_3.6\lib\site-packages\numpy\core\”, line 87, in _mean ret = ret / rcount
    TypeError: unsupported operand type(s) for /: ‘map’ and ‘int’


    Traceback (most recent call last):
    File “C:\Users\XXX\AppData\Local\Continuum\anaconda3\envs\env_python_3.6\lib\site-packages\matplotlib\”, line 168, in get_converter
    if not np.all(xravel.mask):
    AttributeError: ‘numpy.ndarray’ object has no attribute ‘mask’

    During handling of the above exception, another exception occurred:

    Traceback (most recent call last):
    File “”, line 1, in
    File “C:\Users\XXX\AppData\Local\Continuum\anaconda3\envs\env_python_3.6\lib\site-packages\matplotlib\”, line 2659, in hist
    **({“data”: data} if data is not None else {}), **kwargs)
    File “C:\Users\XXX\AppData\Local\Continuum\anaconda3\envs\env_python_3.6\lib\site-packages\matplotlib\”, line 1810, in inner
    return func(ax, *args, **kwargs)
    File “C:\Users\XXX\AppData\Local\Continuum\anaconda3\envs\env_python_3.6\lib\site-packages\matplotlib\axes\”, line 6534, in hist
    self._process_unit_info(xdata=x[0], kwargs=kwargs)
    File “C:\Users\XXX\AppData\Local\Continuum\anaconda3\envs\env_python_3.6\lib\site-packages\matplotlib\axes\”, line 2135, in _process_unit_info
    kwargs = _process_single_axis(xdata, self.xaxis, ‘xunits’, kwargs)
    File “C:\Users\XXX\AppData\Local\Continuum\anaconda3\envs\env_python_3.6\lib\site-packages\matplotlib\axes\”, line 2118, in _process_single_axis
    File “C:\Users\XXX\AppData\Local\Continuum\anaconda3\envs\env_python_3.6\lib\site-packages\matplotlib\”, line 1467, in update_units
    converter = munits.registry.get_converter(data)
    File “C:\Users\XXX\AppData\Local\Continuum\anaconda3\envs\env_python_3.6\lib\site-packages\matplotlib\”, line 181, in get_converter
    converter = self.get_converter(next_item)
    File “C:\Users\XXX\AppData\Local\Continuum\anaconda3\envs\env_python_3.6\lib\site-packages\matplotlib\”, line 187, in get_converter
    thisx = safe_first_element(x)
    File “C:\Users\XXX\AppData\Local\Continuum\anaconda3\envs\env_python_3.6\lib\site-packages\matplotlib\cbook\”, line 1635, in safe_first_element
    raise RuntimeError(“matplotlib does not support generators ”
    RuntimeError: matplotlib does not support generators as input

    • Avatar
      Jason Brownlee July 5, 2019 at 8:04 am #

      Sorry to hear that.

      I can confirm that the code listings work with Keras 2.2.4 and TensorFlow 1.14.0.

      Are you able to confirm that you copied all of the code and that your libraries are up to date?

  67. Avatar
    Royal July 5, 2019 at 5:28 pm #

    1) It works now, many thanks. You might want to include the code in your example above so it works for others:
    import numpy as np
    np_load_old = np.load
    np.load = lambda *a,**k: np_load_old(*a, allow_pickle=True, **k)

    2) The error message resulted from the following code (under # Summarize review length):
    result = map(len, X) # Bad version
    result = [len(x) for x in X] # Good version which creates no errors.

    Did you just correct this? Otherwise I have no explanation why copy-and-paste would have introduced “result = map(len, X)” before.
    Brilliant work!

    • Avatar
      Jason Brownlee July 6, 2019 at 8:27 am #

      Thanks. I am hoping there is a new Keras release around the corner that already incldues the fix.

      The code works as is with Python 3.6 run from the command line. Is it possible you were running in an IDE/Notebook or using an older version of Python?

  68. Avatar
    Royal July 6, 2019 at 6:55 pm #

    I’m using python 3.6.8 in command-line mode with Windows 10.

    I’ve never encountered anyone so dedicated to support their online learning service as you. I’m amazed.

    In summary, thanks to your input, the example works fine now.
    – Dr. Royal Truman

    • Avatar
      Jason Brownlee July 7, 2019 at 7:50 am #

      Thanks, I’m very happy to hear that it’s now working!

  69. Avatar
    Swarupa De August 2, 2019 at 8:44 am #

    Hi Jason.

    I was trying to implement Conv2D for the same thing. I had kept the kernel size as (3,3) however this didn’t seem to work. But as i switched to Conv1D after looking at your example i got it working. Could you let me know why you decided to use 1D and if there are any specific use cases for either.


    • Avatar
      Jason Brownlee August 2, 2019 at 2:36 pm #

      I don’t believe that a conv2d would be appropriate for text input.

      We use a conv1d because we are working with 1d sequence of words.

  70. Avatar
    Liberty November 17, 2019 at 12:17 pm #

    Hi Mr. Jason, could tell me how can I do a prediction with the model picking a single comment out of the dataset as the input?

  71. Avatar
    Ilias P. May 14, 2020 at 4:50 am #

    Jason, thank you for your beautiful lessons. I see that in this example you are not using any sliding window, or timesteps for your input in the lstm model. However, in other lstm examples, and generally in the internet, i have seen that it’s useful to transform the dataset with timesteps, when using lstm models? Is it true? When i need to preproccess the data with timesteps? Thank you in advance

  72. Avatar
    kooni June 30, 2020 at 4:34 pm #

    Thank you for this tutorial
    Is it right to use lstm or cnn-lstm in this dataset?

    • Avatar
      Jason Brownlee July 1, 2020 at 5:51 am #

      You’re welcome.

      A CNN or LSTM with a word embedding would probably most appropriate for this type of problem.

  73. Avatar
    Alexey July 16, 2020 at 10:43 pm #

    Hi Jason,

    Thanks a lot for your blog/tutorial/book, which are excellent.
    I have been struggling trying to make a similar notebook (using imdb data from keras.datasets) work on a TPU(s) by using Google Colab.

    Any advice on how to make the code in this tutorial work on TPU?

    Thank you,

  74. Avatar
    Johan August 13, 2020 at 5:18 pm #

    As comparison, a traditional approach with CountVectorizer and TfidfTransformer reaches 84.26% accuracy with MultinomialNB and 88.68% with LinearSVC (using top 5k words)

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