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Sentiment Analysis of hotel reviews by using Word Embedding, CNN, RNN

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.ipynb_checkpoints
 
 
.gitignore
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CNN,RNN,RMDL_elmoMinor.ipynb
CNN,RNN,RMDL_elmoMinor.ipynb
 
 
CNN,RNN_elmoMinor.ipynb
CNN,RNN_elmoMinor.ipynb
 
 
HAN_of_elmoMinor(1).ipynb
HAN_of_elmoMinor(1).ipynb
 
 
Multi_Channel_CNN_of_elmoMinor.ipynb
Multi_Channel_CNN_of_elmoMinor.ipynb
 
 
README.md
README.md
 
 
RMDL_of_elmoMinor(1).ipynb
RMDL_of_elmoMinor(1).ipynb
 
 
Untitled.ipynb
Untitled.ipynb
 
 
bertCNN.ipynb
bertCNN.ipynb
 
 
bertRNN.ipynb
bertRNN.ipynb
 
 
fasttext.ipynb
fasttext.ipynb
 
 
hotel_review_bert_RMDL.ipynb
hotel_review_bert_RMDL.ipynb
 
 
hotel_review_bert_multichannel_cnn.ipynb
hotel_review_bert_multichannel_cnn.ipynb
 
 
imageSentiment_CNN.ipynb
imageSentiment_CNN.ipynb
 
 
wordEmbeddingCNN_RNN.ipynb
wordEmbeddingCNN_RNN.ipynb
 
 

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Sentiment Analysis of Hotel Reviews

We have done sentiment analysis as a part of Minor Project -

  1. text dataset with Fastext, Elmo and Bert embedding, and trained with CNN,RNN,RMDL,HAN and Multi-channel CNN and RNN.
  2. image dataset sentiment analysis with CNN.

Dataset we have used -

https://www.kaggle.com/jiashenliu/515k-hotel-reviews-data-in-europe (text reviews) https://data.world/crowdflower/image-sentiment-polarity (image reviews)

Here is the colab link of all code-

Text

  1. Fasttext - https://drive.google.com/file/d/1M67TUE9OAGZOiPCToDP-2htiaWpwtaOg/view?usp=sharing

  2. wordEmbedding + CNN - https://drive.google.com/file/d/1PUO-BMeWw8pQ39OpMDg6H8xpw7_gti7g/view?usp=sharing

  3. wordEmbedding + RNN - https://drive.google.com/file/d/1PUO-BMeWw8pQ39OpMDg6H8xpw7_gti7g/view?usp=sharing

  4. Elmo + CNN - https://drive.google.com/file/d/1SGElAzr--LelRL0v8rjUzF5ESYYxx9_j/view?usp=sharing

  5. Elmo + RNN - https://drive.google.com/file/d/1SGElAzr--LelRL0v8rjUzF5ESYYxx9_j/view?usp=sharing

  6. Elmo + RMDL- https://drive.google.com/file/d/1knz1PFJFLhoarVCTxC5MJnCI3AoIlYqe/view?usp=sharing

  7. Bert + CNN - https://drive.google.com/file/d/146KXumr0lXjvAFcV8ogCYc_URUPR0Nqd/view?usp=sharing

  8. Bert + RNN - https://drive.google.com/file/d/1W2U5PPHZo9yXBEbu8wwbbapUCxx4f9US/view?usp=sharing

  9. Bert + RMDL - https://drive.google.com/file/d/1Zmv6TIcSe_qSgHJlXwDkX74gUx_HMHqC/view?usp=sharing

  10. Bert + Multi-channel CNN - https://drive.google.com/file/d/1W_rNyR4CFWoqkWNKyiSXtv8e8_L55OGD/view?usp=sharing

we have issue with following technology
  1. Elmo + Multi-channel CNN - https://drive.google.com/file/d/1HOCEdD4UszgnC5C7vs5E1iJjG5yeWGrV/view?usp=sharing
  2. Elmo + HAN - https://drive.google.com/file/d/1powJSaC4nDhkEWBDFeObJoyJz1lx_bkI/view?usp=sharing

Image

  1. CNN - https://drive.google.com/file/d/1EIbFXMO7E6sU4mtu9HOeuILQb9oP3mTL/view?usp=sharing

Text techonlogies comparison report -

https://docs.google.com/document/d/1ygtTUgWqI61iMYbUV3zfLPHNA92sIefFnjtltoN0sFo/edit?usp=sharing

Reference Code - https://drive.google.com/file/d/1G5GeZNSXbGlu_f_wPXtbQemz7EIbSZI3/view?usp=sharing

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Sentiment Analysis of hotel reviews by using Word Embedding, CNN, RNN

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