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Aspect-based Sentiment Analysis is a more granular approach that identifies sentiment with respect to specific aspects or features mentioned in text rather than the overall sentiment.

Machine Learning in sentiment analysis refers to the use of algorithms that can learn from and make predictions or decisions based on data, such as identifying sentiment trends in text.

RNN is a type of neural network where connections between nodes form a directed graph along a temporal sequence, allowing it to exhibit temporal dynamic behavior, suitable for sentiment analysis in sequential data like text.

A Sentiment Lexicon is a dictionary or database of words labeled with their corresponding sentiment polarity (positive, negative, or neutral).

BERT is a transformer-based machine learning technique for natural language processing pre-training that can improve the context understanding in sentiment analysis tasks.

Text Preprocessing in sentiment analysis involves cleaning and preparing text data for analysis, including tasks like tokenization, stemming, stopword removal, and lemmatization.

Naive Bayes Classifier is a probabilistic machine learning model based on Bayes' Theorem, commonly used in text classification including sentiment analysis.

Polarity refers to the orientation of sentiment expressed in text, typically categorized as positive, negative, or neutral.

Sentiment Analysis is the computational study of people's opinions, sentiments, emotions, and attitudes expressed in text.

Word Embeddings are a type of word representation that allows words with similar meaning to have a similar representation in vector space, beneficial for capturing context in sentiment analysis.

Deep Learning is a subset of machine learning involving neural networks with many layers, which can capture complex relationships in data and are increasingly used for sentiment analysis.

SVM is a supervised learning algorithm used for classification and regression tasks, including sentiment analysis, by finding the hyperplane that best separates different classes.

Sentiment Score is a numerical value assigned to a piece of text that indicates the sentiment polarity and, often, intensity, ranging from highly negative to highly positive.

NLP is a field of computer science focused on the interaction between computers and human language, enabling computers to interpret, generate, and learn from natural language data.