An Improved LSTM-CRF Based Method for Sentiment Analysis Through Knowledge Based Integration

Sentiment analysis is a key element for expert reviews but is at a loss in terms of detection of sarcasm and irony, which distort the actual sentiments. This study presents an approach that combines both syntactic and contextual knowledge to address this problem. Data was preprocessed with tokenization, removal of stop words, lemmatization, and feature extraction with Bidirectional Encoder Representations from Transformers (BERT) embeddings. The high-dimensional output space is handled through the feature selection process of Relief-F, which retains only the most relevant features. The model uses multiple deep learning techniques, wherein LSTM (Long Short-Term Memory) networks combined with Conditional Random Fields (CRF) best shots. Sequential dependency learning gives several advantages toward bettering the accuracy of sentiment classification. It is also capable of identifying subtle distinctions like sarcasm and irony by considering contextual and temporal data, putting the text into one of three categories: positive, negative, or neutral. This advancement is one step ahead of traditional models and provides a more profound insight into the emotion being said in the text.