Sentiment analysis is an active research area in natural language processing. The task aims at identifying, extracting, and classifying sentiments from user texts in post blogs, product reviews, or social networks. In this paper, the ensemble learning model of sentiment classification is presented, also known as CEM (classifier ensemble model). The model contains various data feature types, including language features, sentiment shifting, and statistical techniques. A deep learning model is adopted with word embedding representation to address explicit, implicit, and abstract sentiment factors in textual data. The experiments conducted based on different real datasets found that our sentiment classification system is better than traditional machine learning techniques, such as Support Vector Machines and other ensemble learning systems, as well as the deep learning model, Long Short-Term Memory network, which has shown state-of-the-art results for sentiment analysis in almost corpuses. Our model’s distinguishing point consists in its effective application to different languages and different domains.
Sentiment analysis or opinion mining is a new research field but playing an important role in computer sciences, attracting interests of academia as well as industry. As a field of research, it is closely related to natural language processing, machine learning, text mining and information retrieval. In this paper, we use the machine learning approach to classify hotel service reviews. The results of this phase are integrated into our voice server system that we have successfully developed previously ([31], [32]). As a result, users can consult hotel information via phone calls instead of the keyboard.
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