Online reviews play an important role in influencing buyers' daily purchase decisions. However, fake and meaningless reviews, which cannot reflect users' genuine purchase experience and opinions, widely exist on the Web and pose great challenges for users to make right choices. Therefore, it is desirable to build a fair model that evaluates the quality of products by distinguishing spamming reviews. We present an end-to-end trainable unified model to leverage the appealing properties from Autoencoder and random forest. A stochastic decision tree model is implemented to guide the global parameter learning process. Extensive experiments were conducted on a large Amazon review dataset. The proposed model consistently outperforms a series of compared methods.
Twitter is an important source of information that people frequently contribute to and rely on for emerging topics, public opinions, and event awareness. Crisis-relevant tweets can potentially avail a magnitude of applications such as helping authorities and governments become aware of situations and thus offer better responses. One major challenge toward crisis-awareness in Twitter is to identify those tweets that are relevant to unseen crises. In this article, we propose an automatic labeling approach to distinguishing crisis-relevant tweets while differentiating source types (e.g., government or personal accounts) simultaneously. We first analyze and identify tweet-specific linguistic, sentimental, and emotional features based on statistical topic modeling. Then, we design a novel correlative convolutional neural network which uses a shared hidden layer to learn effective representations of the multi-faceted features. The model can discover salient information while being robust to the variations and noises in tweets and sources. To obtain a bird’s-eye view of a crisis event, we further develop an approach to automatically summarize key information of identified tweets. Empirical evaluation on a real Twitter dataset demonstrates the feasibility of discerning relevant tweets for an unseen crisis. The applicability of our proposed approach is further demonstrated with a crisis aider system.
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