Sentimental Comment Analyzer

• Author(s):

  Dharanidharan N | Dharmesh S | Dharshana K | Gokulpriya J | Subasri N

• Keywords:

  PCB Defect Detection, YOLOv8, Deep Learning, Real-Time Inspection, Computer Vision, Flask Web Application, Automated Optical Inspection (AOI), Surface Defect Classification, Industrial Automation, Smart Manufacturing.

• Abstract:

  In The Digital Age, Social Media Platforms Like YouTube Have Become Central To Online Interaction, With Vast Amounts Of User-generated Content That Reflect Diverse Opinions, Feedback, And Sentiments. Analyzing This Content Offers Powerful Insights, But Manually Processing Large Volumes Of Comments Can Be Time-consuming And Inefficient. Leveraging Artificial Intelligence (AI) In Sentiment Analysis Has Transformed How We Interpret Online Sentiment, Enabling Faster, More Accurate Categorization Of User Feedback. This Paper Presents The Architecture And Benefits Of An AI-powered Sentiment Comment Analyzer Designed To Automatically Assess User Sentiment On Platforms Like YouTube, Enhancing Content Creators' And Analysts' Understanding Of Audience Perceptions.The Proposed System Integrates AI Features Like Natural Language Processing For Extracting Context And Tone From Text, Machine Learning Models For Categorizing Comments Into Positive, Negative, Or Neutral Sentiments, And Real-time Analytics For Tracking Sentiment Shifts Over Time. Additionally, It Supports Customizable Sentiment Thresholds, Automated Alerts For Significant Feedback Changes, And Visual Sentiment Reports To Provide Actionable Insights.Through AI-driven Sentiment Analysis, The System Ensures Improved Insight Accuracy, Better Audience Engagement Strategies, And Rapid Response Capabilities. Our Platform’s Sentiment Analysis Tools Leverage Adaptive Machine Learning Algorithms That Refine Their Sentiment Detection Accuracy Over Time, Ensuring The Analysis Remains Relevant And Precise. This System Offers A Comprehensive, Data-rich Solution For Interpreting Social Media Feedback, Meeting The Growing Need For Nuanced Audience Understanding In Today’s Digital Landscape.

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Other Details

• Paper id:

  IJSARTV11I10104075

• Published in:

  Volume: 11 Issue: 10 October 2025

• Publication Date:

  2025-10-05

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