MACHINE LEARNING-BASED SYSTEM FOR DETECTING EMOTIONS

Abstract

ABSTRACT A machine learning-based system 100 for detecting emotions, comprising a processing module (102) for text data cleaning and tokenization, a feature extraction module (104) for deriving linguistic features from text, a classification module (106) with machine learning algorithms for emotion detection and an analytics module (108) for interpreting and visualizing emotional trends. <>

Specification

Description:FIELD OF THE DISCLOSURE
[0001] This invention generally relates to the field of emotional analysis in machine learning, specifically concerning automated recognition and analysis of emotions such as depression and sarcasm. The approach leverages R programming for data analysis, aiming to facilitate early detection and nuanced understanding of emotional states in individuals based on text data.
BACKGROUND

[0002] The subject matter discussed in the background section should not be assumed to be prior art merely as a result of its mention in the background section. Similarly, a problem mentioned in the background section or associated with the subject matter of the background section should not be assumed to have been previously recognized in the prior art. The subject matter in the background section merely represents different approaches, which in and of themselves may also correspond to implementations of the claimed technology.
[0003] In recent years, the need to understand and interpret human emotions t , Claims:1. A machine learning-based system 100 for detecting emotions, comprising:
a processing module (102) for text data cleaning and tokenization,
a feature extraction module (104) for deriving linguistic features from text,
a classification module (106) with machine learning algorithms for emotion detection, and
an analytics module (108) for interpreting and visualizing emotional trends.

2. The system 100 as claimed in claim 1, wherein the processing module (102) utilizes R programming to clean and format text data, including removing punctuation, standardizing casing, and eliminating irrelevant content.

3. The system 100 as claimed in claim 1, wherein the classification module (106) applies machine learning algorithms such as Support Vector Machine (SVM) and Naive Bayes using R's caret.

4. The system 100 as claimed in claim 1, wherein the analytics module (108) visualizes emotional tendencies, highlighting patterns indicative of depression and sarcasm within the analysed text.

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