International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 12 Issue: 12 | Dec 2025
p-ISSN: 2395-0072
www.irjet.net
Sentiment analysis for business purpose Sheetal Kokatnur1, Mohan M2, Ravi U3, Siddharth K4, Suraj C5 1Assistant Professor, SG Balekundri Institute of Technology, Belagavi, Karnataka, India 2,3,4,5, Student, SG Balekundri Institute of Technology, Belagavi, Karnataka, India
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Abstract - In the modern digital era, organizations are surrounded by massive volumes of customer-generated textual data originating from online reviews, social media platforms, feedback forms, and customer support interactions. While this data holds valuable insights about customer opinions and emotions, manual analysis is inefficient, time-consuming, and often biased. To address this challenge, this project presents the development of an automated sentiment analysis system designed specifically for business-oriented decision-making. The proposed system employs Natural Language Processing (NLP) techniques combined with machine learning–based sentiment scoring approaches such as VADER and Text Blob to classify textual feedback into positive, negative, and neutral categories. A structured preprocessing pipeline is implemented, including text cleaning, tokenization, stop- word elimination, and lemmatization, to improve classification accuracy by reducing linguistic noise.
1. INTRODUCTION With the rapid growth of digital platforms, customers continuously express their opinions through reviews, social media posts, surveys, and online discussions. These expressions provide organizations with direct insights into customer satisfaction, expectations, and emerging issues. However, the exponential increase in such unstructured textual data has made traditional manual analysis methods impractical, leading to delays in decision- making and potential misinterpretation of customer intent. Sentiment analysis also referred to as opinion mining, is a branch of Natural Language Processing (NLP) that focuses on identifying and categorizing emotions expressed in text. By automatically determining whether feedback reflects positive, negative, or neutral sentiment, businesses can better understand public perception of their products and services. This capability enables organizations to refine marketing strategies, improve product features, and enhance customer service responsiveness.
1.1 Research Objectives The primary objectives of this research project are:
To design an NLP-based system capable of classifying customer feedback into positive, negative, and neutral sentiments.
To construct an efficient text preprocessing pipeline that handles noise removal, tokenization, lemmatization, and normalization.
To support bulk data processing for large-scale business datasets without performance degradation.
To generate intuitive visual insights such as pie charts, bar graphs, and word clouds for better interpretation.
1.2 Operational Modes
Lexicon-Based Approach
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Select colors via gestures
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Clear complete canvas
Control brush thickness
Smooth, real-time output on browser
Machine Learning–Based Approach
ML-powered gesture recognition
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