Recommendation Chart of Domains for Cross-Domain Sentiment Analysis: Findings of A 20 Domain Study
Table 1: Accuracy percentage for all train-test pairs. Domains on rows are source domains and columns are target domains. Domain labels are D1: Amazon Instant Video, D2: Automotive, D3: Baby, D4: Beauty, D5: Books, D6: Clothing Accessories, D7: Electronics, D8: Health, D9: Home, D10: Kitchen, D11: Movies TV, D12: Music, D13: Office Products, D14: Patio, D15: Pet Supplies, D15: Shoes, D16: Software, D17: Sports Outdoors, D18: Tools Home Improvement, D19: Toys Games, D20: Video Games.
['[EMPTY]', '[BOLD] D1', '[BOLD] D2', '[BOLD] D3', '[BOLD] D4', '[BOLD] D5', '[BOLD] D6', '[BOLD] D7', '[BOLD] D8', '[BOLD] D9', '[BOLD] D10', '[BOLD] D11', '[BOLD] D12', '[BOLD] D13', '[BOLD] D14', '[BOLD] D15', '[BOLD] D16', '[BOLD] D17', '[BOLD] D18', '[BOLD] D19', '[BOLD] D20']
We normalize the dataset by removing numerical values, punctuations, stop words, and changing all words to the lower case. To train the sentiment classifier, we use an LSTM-based sentiment classifier. It consists of an embedding layer initialized with pre-trained GloVe word embeddings of 100 dimensions. We train this model for 20 epochs with a dropout factor of 0.2 and use sigmoid as the activation function. For In-domain sentiment analysis, we report a 5-fold classification accuracy with a train-test split of 8000 and 2000 reviews.