A Ruby gem for simple sentiment analysis that classifies text as positive, negative, or neutral based on configurable thresholds.
Sentimental is a Ruby gem that performs sentiment analysis on text by tokenizing sentences and assigning numerical sentiment scores. It classifies text as positive, negative, or neutral based on configurable thresholds and supports custom sentiment dictionaries.
Ruby developers who need simple sentiment analysis capabilities without complex machine learning setups, particularly those working with text processing or social media analysis.
It provides a lightweight, configurable alternative to heavier NLP libraries with straightforward dictionary-based scoring and adjustable sensitivity thresholds.
Simple sentiment analysis with Ruby
Open-Awesome is built by the community, for the community. Submit a project, suggest an awesome list, or help improve the catalog on GitHub.
Allows fine-tuning sensitivity for sentiment classifications, as demonstrated in the README with examples like setting thresholds to 0.1 or 0.9 to adjust positive/neutral/negative ranges.
Enables loading user-defined sentiment dictionaries in a simple text format, making it adaptable to specific domains or vocabularies without complex setup.
Supports multi-word phrase analysis by specifying n-gram size during initialization, improving context-aware scoring for phrases like 'very happy'.
Provides a straightforward Ruby interface with minimal dependencies, ideal for quick prototyping or adding basic sentiment features without ML overhead.
Relies on static dictionaries and basic tokenization, making it ineffective for sarcasm, irony, or evolving language compared to machine learning-based approaches.
Last documented change was in 2013, indicating potential staleness and lack of modern features like emoji handling or improved performance optimizations.
Primarily focused on English with minimal slang additions; lacks built-in dictionaries or tools for other languages, limiting its applicability globally.