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