"Sentiment analysis is the use of natural language processing, text analysis, computational linguistics, and biometrics to systematically identify, extract, quantify, and study affective states and subjective information."
Detecting and classifying the emotional sentiment expressed in a piece of text.
Natural Language Processing (NLP): NLP is a subfield of computer science that deals with the interactions between humans and computers in natural language.
Machine Learning Algorithms: Machine learning algorithms are used to automatically learn patterns and relationships in data.
Text Pre-processing: Text pre-processing involves cleaning, normalizing, and transforming text data into a format that can be easily analyzed by algorithms.
Tokenization: Tokenization is the process of breaking a text into smaller units such as words, phrases, or sentences.
Part-of-Speech (POS) Tagging: POS tagging is the process of labeling each word in a sentence with its respective part of speech.
Named Entity Recognition (NER): NER is the process of identifying and classifying named entities in text, such as people, organizations, and locations.
Lexicon-based Sentiment Analysis: Lexicon-based sentiment analysis involves using pre-defined dictionaries of words and their associated sentiment polarity to identify the sentiment of a text.
Machine Learning-based Sentiment Analysis: Machine learning-based sentiment analysis involves training models to automatically classify the sentiment of a text using labeled data.
Feature Engineering: Feature engineering is the process of selecting and transforming features that are relevant for a specific NLP task, such as sentiment analysis.
Evaluation Metrics: Evaluation metrics are used to measure the performance of a sentiment analysis model, such as accuracy, precision, recall, and F1-score.
Bias and Fairness in Sentiment Analysis: Bias and fairness are important considerations in sentiment analysis, as the use of biased or unfair data can lead to inaccurate results and harmful consequences.
Ethics of Sentiment Analysis: The ethical implications of sentiment analysis, such as privacy concerns and potential misuse, should also be considered when working with NLP technology.
Document-level sentiment analysis: This type of sentiment analysis examines the overall sentiment of a complete document, such as an article or review.
Sentence-level sentiment analysis: Sentence-level sentiment analysis breaks down a document into individual sentences and analyzes the sentiment of each one.
Aspect-based sentiment analysis: This type of sentiment analysis breaks down a document or sentence into a variety of aspects or topics and focuses on the sentiment of each individual aspect.
Domain-specific sentiment analysis: Domain-specific sentiment analysis examines sentiment within a certain context, such as a particular industry or subject matter.
Comparative sentiment analysis: This type of sentiment analysis compares the sentiment of one entity or group to another entity or group.
Fine-grained sentiment analysis: Fine-grained sentiment analysis examines the nuances and complexities of different types of sentiment, such as positive, negative, neutral, or mixed.
Emotion detection: Sentiment analysis can also be used to detect specific emotions, such as anger, fear, joy, or sadness.
Intent analysis: Intent analysis focuses on understanding the intentions of the writer or speaker, and whether their sentiment is positive or negative in relation to those intentions.
Multilingual sentiment analysis: This type of sentiment analysis works with multiple languages, providing the same sentiment analysis across all languages.
Opinion mining: Opinion mining is a type of sentiment analysis that focuses on identifying and extracting opinions and sentiments from unstructured text.
"(also known as opinion mining or emotion AI)"
"Sentiment analysis is widely applied to voice of the customer materials such as reviews and survey responses, online and social media, and healthcare materials."
"For applications that range from marketing to customer service to clinical medicine."
"With the rise of deep language models, such as RoBERTa, also more difficult data domains can be analyzed."
"E.g., news texts where authors typically express their opinion/sentiment less explicitly."
"To systematically identify, extract, quantify, and study affective states and subjective information."
"The use of natural language processing, text analysis, computational linguistics, and biometrics."
"Voice of the customer materials such as reviews and survey responses, online and social media, and healthcare materials."
"To study affective states and subjective information."
"Applications that range from marketing to customer service to clinical medicine."
"With the rise of deep language models, such as RoBERTa."
"News texts where authors typically express their opinion/sentiment less explicitly."
"Marketing, customer service, and clinical medicine."
"Sentiment analysis (also known as opinion mining or emotion AI)"
"Natural language processing, text analysis, computational linguistics, and biometrics."
"Affective states and subjective information."
"Healthcare materials for applications that range from marketing to customer service to clinical medicine."
"Computational linguistics contributes to systematically identify, extract, quantify, and study affective states and subjective information."
"The rise of deep language models, such as RoBERTa."