Sentilex is a sentiment analysis project that determines whether a given text expresses positive, negative, or neutral sentiment using a lexicon-based approach. The system evaluates input text by comparing words against a predefined sentiment dictionary, assigning scores based on word polarity, and aggregating these scores to produce an overall sentiment classification. This rule-driven method avoids machine learning or NLP models, ensuring transparency, simplicity, and fast execution while still providing meaningful sentiment insights for reviews, feedback, and short text inputs.

🔹 Use Cases

Sentilex can be applied in multiple real-world scenarios where quick and lightweight sentiment evaluation is required:

• Customer Feedback Analysis
  Analyzes textual feedback from users to determine overall sentiment and identify satisfaction trends.

• Product & Service Review Evaluation
  Helps assess public opinion on products or services by classifying user reviews as positive, negative, or neutral.

• Social Media Sentiment Monitoring
  Tracks general sentiment in comments, posts, or discussions related to brands, events, or topics.

• Survey & Opinion Analysis
  Automatically evaluates sentiment from open-ended survey responses, reducing manual review effort.

• Educational & Academic Applications
  Demonstrates rule-based sentiment analysis for learning purposes without relying on machine learning or NLP models.

• Content Moderation Assistance
  Supports basic filtering by identifying highly negative or critical textual content.

• Lightweight Systems
  Suitable for environments with limited computational resources where complex models are not practical.