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Sentiment-Analysis-CNN

Text classification using Convolutional Neural Networks

Dataset Description

The dataset contains reviews from the following 3 websites, Amazon, Imdb, Yelp. There are 1000 reviews for each website, 500 of which are positive. In total we have 1500 positive reviews and 1500 negative reviews.

Update: Added new dataset consisting of movie reviews with 5531 positive training examples and 5531 negative training examples.

Model Description

The model takes inspiration from the paper, "Sentence Classification using Convolutional Neural Networks" by Yoon Kim. Paper

Kim CNN: Kim CNN

CONVOLUTIONAL LAYER

  • Multiple filters of varying window size convolved over each training example.
  • Each filter generates a feature map.
  • Filters with different window sizes capture context and relation between words.

MAX-POOLING LAYER

  • Max-pooling operation performed on each feature map to get one feature per filter.
  • The idea is to capture the most important feature necessary for classification.
  • Naturally deals with variable length sentences

FULLY CONNECTED LAYER

SOFTMAX LAYER

  • Probability distribution over labels obtained.

Hyperparameter Tuning

Tunable hyperparameters:

  1. Word vector size (embedding size)
  2. Sequence length (after padding or truncation)
  3. Filter sizes
  4. Number of filters of each type
  5. Learning rate
  6. Reguralization constant
  7. Num_epochs
  8. minibatch_size

Learning Curves

Batch size too small Near optimal hyperparameters 1)Batch size too small. 2)Near optimal hyperparameters

Results

1

2

Instructions for use:

  1. Data_prepartion.ipynb used to convert tab separated data into csv format [sentence,category].
  2. Final_Code_CNN.ipynb uses csv input as mentioned above and trains the CNN model.
  3. CNN_code_raw folder contains older versions of the Final_Code_CNN with various intermediate blocks to print output for better visualization, understanding and debugging. (Final_Code_CNN contains only necessary blocks to train the model.)
  4. If trying to reproduce results on the same dataset, no need to run Data_preparation.ipynb, data in proper format already present in Processed_Data folder.

More work to be done

  1. Domain adaptation: I plan to train the model using data from a specific domain like movie reviews and then test it on reviews from other domains like Electronics, Food, Travel etc.
  2. Work on CNN + LSTM. Both models will be trained independently and the output will be concatenated.