Identifies and categorizes the emotions expressed in a piece of text.
Natural language processing (NLP): The field that deals with the interaction between computers and human language.
Machine learning: The method of enabling computers to learn from data and improve their performance over time.
Text preprocessing: The process of cleaning, filtering, and transforming raw text data to make it suitable for analysis.
Lexicon-based approaches: A method of sentiment analysis that uses pre-existing dictionaries or word lists to assign positive, negative or neutral sentiment to a text.
Machine learning-based approaches: A method of sentiment analysis that uses machine learning models to automatically identify and classify sentiment in a text.
Feature engineering: The process of selecting and extracting relevant features from raw text data to improve the accuracy of sentiment analysis models.
Sentiment classification: The process of categorizing a text as positive, negative, or neutral.
Part-of-speech tagging (POS): The process of labeling each word in a text with its corresponding part of speech, such as noun, verb, adjective, etc.
Named Entity Recognition (NER): The process of identifying and categorizing named entities in a text, such as people, organizations, locations, etc.
Sentiment lexicons: Pre-existing lists of words with assigned sentiment scores that are used in lexicon-based sentiment analysis.
Deep learning: A method of machine learning inspired by the structure and function of the human brain that uses neural networks to learn from data.
Word embeddings: A technique of representing words as numerical vectors, making it possible to perform mathematical operations on them.
Topic modeling: The process of categorizing a set of documents into different topics based on their content.
Emotion detection: A method of sentiment analysis that identifies the specific emotions expressed in a text, such as happiness, anger, sadness, etc.
Irony detection: The ability to recognize and interpret the use of irony and sarcasm in a text, which can greatly influence its sentiment.
Document-level Sentiment Analysis: This type of sentiment analysis is used to analyze the overall sentiment of a piece of text, such as a news article, blog post, or customer feedback.
Sentence-level Sentiment Analysis: Sentence-level sentiment analysis is used to analyze the sentiment of individual sentences within a larger piece of text.
Aspect-based Sentiment Analysis: This type of sentiment analysis is used to analyze the sentiment of specific aspects or features mentioned in a piece of text. For example, a review of a restaurant might analyze the sentiment of the food, service, ambiance, etc.
Entity-based Sentiment Analysis: Entity-based sentiment analysis is used to analyze the sentiment of specific entities mentioned in a piece of text, such as a person, place, or product.
Multilingual Sentiment Analysis: This type of sentiment analysis is used to analyze sentiment in multiple languages.
Emotion Detection: Emotion detection is used to analyze the emotions expressed in a piece of text, such as joy, sadness, anger, or fear.
Opinion Mining: Opinion mining is used to extract opinions and attitudes from a piece of text, such as positive, negative, or neutral opinions.
Intent Analysis: Intent analysis is used to determine the intention behind a piece of text, such as whether it is a complaint, a request for information, or a suggestion.
Irony and Sarcasm Detection: Irony and sarcasm detection is used to identify instances where the writer or speaker is using language in a way that is opposite of what they really mean.
Comparative Sentiment Analysis: Comparative sentiment analysis is used to analyze sentiment between different entities or aspects, such as comparing the sentiment towards two different products.