A field of AI focused on the interaction between computers and human language, enabling machines to understand and process text.

The computational task of identifying and categorizing opinions expressed in text, often as positive, negative, or neutral.

A popular Python library for working with human language data, providing tools for text processing and analysis.

An advanced Python library for NLP, designed for performance and usability, suitable for large-scale applications.

The process of cleaning and preparing text data for analysis, including tokenization, normalization, and removing noise.

The process of breaking text into smaller units, such as words or phrases, which can be analyzed individually.

A measure of sentiment orientation in text, indicating whether the sentiment is positive, negative, or neutral.

The degree to which a text expresses personal opinions or feelings rather than objective facts.

Quantitative measures used to assess the performance of a sentiment analysis model, including accuracy and F1 score.

A metric that indicates the proportion of correct predictions made by a sentiment analysis model.

A measure that combines precision and recall, providing a balance between false positives and false negatives.

The process of gathering text data from various sources, such as social media, for analysis.

The graphical representation of data trends, helping to communicate findings effectively.

The process of examining model errors to identify patterns and improve model performance.

Techniques used to enhance the performance of a sentiment analysis model, such as hyperparameter tuning.

A series of data processing steps that transform raw text into a format suitable for analysis.

A method in sentiment analysis that relies on predefined rules and lexicons to determine sentiment.

A method that uses algorithms to learn from data and make predictions about sentiment.

The process of adjusting the parameters of a machine learning model to optimize its performance.

A technique used to evaluate the generalizability of a model by partitioning data into training and testing sets.

The examination of user-generated content on social media platforms to derive insights and trends.

An interdisciplinary field that uses scientific methods, algorithms, and systems to extract knowledge from data.

A subset of AI that focuses on building systems that can learn from and make predictions based on data.

The process of creating and maintaining a brand's image and reputation, often using sentiment analysis for insights.

Plans developed to promote products or services, informed by data analysis and consumer sentiment.