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Aspect-weighted-Cross-Domain-Sentiment-Analysis: A pytorch implementation of cross domain sentiment analysis, using domain ontologies incorporated in a neural architecture

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Aspect-weighted sentiment analysis using domain ontology and deep neural network

We calculate aspect scores using two approaches:

  1. conditional probability from dataset
  2. a domain ontology

We incorporate these scores into our neural architecture to find the sentiment of a textual review. The scores are used to initialize a trainable layer of the neural architecture.

Domain: Restaurant, Movie, Music, Uber Rides

Steps to run

  1. Run glove_embeddings.py if corresponding pickle files are already not created
  2. Run preprocessing.py if corresponding pickle files are already not created
  3. Run model.py

P.S: Read the commented introduction in each of the files mentioned to run the commands correctly.

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Aspect-weighted-Cross-Domain-Sentiment-Analysis: A pytorch implementation of cross domain sentiment analysis, using domain ontologies incorporated in a neural architecture

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