METHOD FOR TEXT-BASED EMOTION ANALYSIS

Abstract

ABSTRACT A method (100) for text-based emotion analysis. Further, the method comprising collecting a dataset of textual data with associated emotional labels from a publicly accessible source. Further, the method (100) comprising the steps of pre-processing the collected textual data, including tokenization, lemmatization, and removal of stop words. Further, the method (100) comprising the steps of extracting features from the pre-processed text data using techniques selected from the group consisting of Term Frequency-Inverse Document Frequency (TF-IDF) and Count Vectorization. Further, the method (100) comprising the steps of building and evaluating multiple machine learning classification models. Further, the method (100) comprising the steps of deploying the model with the highest performance in a web-based application that allows users to input text and receive predicted emotional states as output.

Specification

Description:FIELD OF THE DISCLOSURE
[0001] This invention generally relates to the field of text analysis and natural language processing, and in particular relates to a method for performing emotion analysis on textual data using machine learning techniques to enhance understanding of emotional states in various applications such as customer service, mental health monitoring, and social media analysis.
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] The increasing volume of textual data generated in the digital age presen , Claims:1. A method (100) for text-based emotion analysis, the method comprising the steps of:
collecting a dataset of textual data with associated emotional labels from a publicly accessible source;
pre-processing the collected textual data, including tokenization, lemmatization, and removal of stop words;
extracting features from the pre-processed text data using techniques selected from the group consisting of Term Frequency-Inverse Document Frequency (TF-IDF) and Count Vectorization;
building and evaluating multiple machine learning classification models; and
deploying the model with the highest performance in a web-based application that allows users to input text and receive predicted emotional states as output.
2. The method (100) as claimed in claim 1, wherein the feature extraction utilizes a Count Vectorization method to convert the pre-processed text into a numerical format suitable for machine learning algorithms.

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