Table of Contents

Namespace LMKit.TextAnalysis

Classes

Categorization

A class designed to handle custom classification of plain text content.

EmotionDetection

Represents a specialized class for analyzing emotional tones in text.
It can identify a range of emotions, such as happiness, sadness, anger and fear, among others.
This class is designed to be used in applications where understanding emotional context is crucial, such as in customer feedback analysis, social media monitoring, or mental health assessment tools.

KeywordExtraction

A class designed to handle keyword extraction tasks. This class provides functionality to extract a specified number of the most important keywords or phrases from a given piece of content, respecting constraints such as maximum n-gram size.

KeywordExtraction.KeywordItem

Represents a single extracted keyword item. This is a read-only value container holding a single keyword string.

SarcasmDetection

Provides functionality for detecting sarcasm in text. This class is designed for applications where understanding sarcasm context is crucial, such as customer feedback analysis, social media monitoring, or mental health assessment tools.

SentimentAnalysis

Provides functionality for performing sentiment analysis on plain text, designed to assess and categorize emotional tone.

Enums

EmotionDetection.EmotionCategory

Enumerates a set of emotion categories for analytical and classification tasks.

EmotionDetection.TrainingDataset

Enumeration representing the built-in training datasets available for fine-tuning language models (LLMs) for sentiment analysis using LMKit.

SarcasmDetection.TrainingDataset

Enumeration representing built-in training datasets available for fine-tuning language models (LLMs) for sarcasm detection using LMKit.

SentimentAnalysis.SentimentCategory

Enumerates various sentiment categories for classification purposes.

SentimentAnalysis.TrainingDataset

Enumeration representing the built-in training datasets available for fine-tuning language models (LLMs) for sentiment analysis using LMKit.