Sentiment analysis is an NLP technique to classify the sentiment (positive, negative, or neutral) of text data. It involves processing textual input, classifying sentiment using techniques like lexicon-based, machine learning, or deep learning models, and has applications in business, social media monitoring, finance, and healthcare. It helps understand public sentiment, customer satisfaction, and market trends.
BERT is pre-trained on a large text corpus using tasks like masked language modeling and next sentence prediction. Fine-tuning on specific tasks involves adjusting the final layers of the pre-trained BERT model which allows us to adapt it to specific NLP tasks such as text classification, named entity recognition, sentiment analysis, and question answering.
For LSTM Model, the Embedding Layer converts tokens into dense vectors to capture word similarities. The LSTM Layer processes sequences, maintaining hidden states for long-term dependencies. A Dropout Layer prevents overfitting by randomly setting input units to zero. The Fully Connected Layer transforms LSTM output, and a Sigmoid Activation Function squashes values to predict sentiment probabilities. This architecture effectively analyzes text for sentiment classification.
Note: Above image is not correct representation of LSTM model defined in my notebook. It is just a representation of how LSTM works on text sentiment analysis.
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