The process of determining the emotional tone behind a body of text, often used to gauge public opinion.

Techniques used to clean and transform raw data into a suitable format for analysis, crucial for effective machine learning.

A subset of AI that enables systems to learn from data, improving their performance on tasks without explicit programming.

The process of breaking text into individual units, such as words or phrases, to facilitate analysis.

A supervised machine learning algorithm used for classification tasks, effective in high-dimensional spaces.

A simple probabilistic classifier based on applying Bayes' theorem, often used for text classification.

A lightweight web framework for Python, ideal for building simple web applications quickly.

A high-level Python web framework that encourages rapid development and clean, pragmatic design.

An approach to analyzing data sets to summarize their main characteristics, often using visual methods.

The graphical representation of data to identify patterns, trends, and insights effectively.

A technique for assessing how the results of a statistical analysis will generalize to an independent data set.

The process of optimizing the parameters that govern the training process of machine learning algorithms.

The process of assessing the performance of a machine learning model using metrics such as accuracy and F1 score.

The overall experience a user has when interacting with a product, particularly in terms of ease of use and satisfaction.

Dynamic graphical representations of data that allow users to explore and manipulate the data.

The process of correcting or removing inaccurate, incomplete, or irrelevant data from a dataset.

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

Quantitative measures used to assess the effectiveness of a machine learning model.

The process of making a software application available for use, often on a web server.

Information provided by users regarding their experience with a product, used to improve future versions.

A series of data processing steps that involve data collection, cleaning, transformation, and storage.

Software applications used to create graphical representations of data, such as Matplotlib or Tableau.

An assessment of the completed project, focusing on the implementation and outcomes of the sentiment analysis tool.