Aspect-level sentiment classification is a method to determine the opposite the sentiment of all aspects within an identical sentence. It is more challenging than the classification of sentiment for text, in that it is more precise. The current methods that define the job as predicting the intensity of the sentiments over a specific (line of sentence) pairs, often miss the connection with the features' polarity in sentiment and their fundamental. In this paper, we present an approach to sequence prediction that incorporates the sentiment and polarity Fusion module that forecasts in a sequential fashion the sentiments of the negative and positive from a sentence with every aspect. In addition, we employ the sequence prediction attention technique to track what is being paying attention to, which prevents constant attention to context words that have high sentiment polarity when it comes to forecasting the polarity of various aspects. Test results from five benchmarking collections demonstrate that our model is superior to several base models by a significant margin. We also show that it is a superior model. Relationship between the sentimental aspect's polarity is useful to resolve the aspect-level classification.

Aspect, Sentiment classification, Sequence, reviews, social networking.