Aspect-Based Sentimental Analysis On Social Media Data Using Deep Learning Methods

Abstract

Nowadays, reviews related to the products given by users and experts in social media are significant references to help product manufacturers make better decisions about prompting items and placing them in the real world. So analysing aspects such as productrelated sentiments is an important task. The target of aspect-based opinion analysis is to determine the emotional valence of each aspect phrase included inside a given sentence. However, the earlier models make the error of identifying irrelevant contextual terms as cues for determining aspect sentiment. They also overlook syntactical limitations and longrange sentiment dependencies. Hence, we assumed a model using LSTM with GCN to distinguish engagers' review opinions under various aspects with three different labels: negative, neutral, and positive. The proposed model LSTM+GCN shows better performance than other models in terms of 4% with precision, 4% with recall, and 5% with accuracy.