Aspect Based Sentiment Analysis using Deep Learning Models - Bhagyashree Bappanna; Ambika Shetkar; Bavana. Hanisha; Manchem Asha priya (Utilitas Mathematica, 2025)
Sentiment analysis is an important task in natural language understanding and has a wide range of real-world applications. In sentiment analysis, the opinion is evaluated to its positivity, negativity and neutrality with respect to the complete document or object. But this level of analysis does not provide the necessary detailed information for many applications. To obtain more fine-grained analysis, Aspect Based Sentiment Analysis is introduced. Aspect Based Sentiment analysis introduces a suite of problems which require deeper NLP capabilities and also produces a rich set of results. This paper discusses about the aspect-based sentiment analysis classification using Deep learning models – Keras sequential model, LSTM model, BERT model using ktrain and multilabel multiclass BERT model using Hugging face transformers for sentiment and aspect classification.