Constructing domain-dependent sentiment dictionary for sentiment analysis

Sentiment dictionary is of great value to sentiment analysis, which is used widely in sentiment analysis compositionality. However, the sentiment polarity and intensity of the word may vary from one domain to another. In this paper, we introduce a novel approach to build domain-dependent sentiment dictionary, SentiDomain. We propose a weak supervised neural model that aims at learning a set of sentiment clusters embedding from the sentence global representation of the target domain. The model is trained on unlabeled data with weak supervision by reconstructing the input sentence representation from the resulting representation. Furthermore, we also propose an attention-based LSTM model to address aspect-level sentiment analysis task based on the sentiment score retrieved from the proposed dictionary. The key idea is to weight-down the non-sentiment parts among aspect-related information in a given sentence. Our extensive experiments on both English and Chinese benchmark datasets have shown that compared to the state-of-the-art alternatives, our proposals can effectively improve polarity detection.