OPINION MINING SYSTEM

Abstract

ABSTRACT An opinion mining system (100), the system (100) comprising: a data preprocessing module for cleaning and preparing raw text data; a feature extraction module for transforming text data into numerical features; a machine learning model for classifying sentiment; an output module for categorizing and presenting results; wherein the system (100) is configured to enhance sentiment analysis accuracy and scalability.

Specification

Description:FIELD OF THE DISCLOSURE
[0001] This invention generally relates to a field of data processing and, in particular, to the design and implementation of an opinion mining system using machine learning techniques to enhance sentiment analysis and opinion categorization.
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] Conventional sentiment analysis techniques often rely on rule-based systems and simple keyword matching to determine the sentiment of a given text. These methods can be limited in accuracy and fail to , Claims:1. An opinion mining system (100), the system (100) comprising:
a data preprocessing module for cleaning and preparing raw text data;
a feature extraction module for transforming text data into numerical features;
a machine learning model for classifying sentiment;
an output module for categorizing and presenting results;
wherein the system (100) is configured to enhance sentiment analysis accuracy and scalability.

2. The system (100) as claimed in claim 1, wherein the data preprocessing module removes noise from raw text data to ensure clean and consistent input for the machine learning model.

3. The system (100) as claimed in claim 1, wherein the feature extraction module converts preprocessed text into numerical features using techniques such as word embedding or TF-ID.

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