Sentiment Analysis Prototype

Creative DirectionROC LabUI/UX

In November 2016, Riot of Colors developed an AI-powered Sentiment Analysis Prototype, leveraging natural language processing (NLP) to classify news sentiment in real time. Using advanced SentiWord algorithms, the system analyzed live RSS feeds, categorizing content as positive, negative, or neutral. Built on Node.js with asynchronous processing, it provided efficient, real-time insights into media sentiment. This early AI initiative laid the foundation for future innovations in content analysis, automated reporting, and digital engagement, shaping how brands and organizations leverage AI for data-driven decision-making.

Riot of Colors

2016

Riot of Colors developed a Sentiment Analysis Prototype, which laid the foundation for future AI-driven analysis projects. This tool utilized natural language processing (NLP) to evaluate sentiment in news articles, classifying them as positive, negative, or neutral.

Key Features & Approach

Design & Interface
- Fully responsive interface using Bootstrap 4 (Alpha 5) with Flexbox.
- Users could select “Top Stories” or filter by category.
- Support for analyzing 10 or 20 news items at a time.
Sentiment Analysis Engine
- Evaluated three sentiment tools: AFINN, Opinion Lexicon, and SentiWord.
- Chose SentiWord due to its contextual sophistication and extensive vocabulary (>100,000 words).
- Implemented weighted sentiment scoring from 0 to 1 for classification:
  - Positive: Above 0.63
  - Neutral: Between 0.34 and 0.63
  - Negative: Below 0.33
Backend Implementation
- Processed news data asynchronously to optimize performance.
- Parsed and formatted news articles dynamically from RSS feeds.
- No database integration; sentiment analysis was performed per request.

Challenges & Learnings

Data limitations: Google’s RSS feeds lacked a dedicated “society” category and occasionally returned inconsistent article counts.
Performance concerns: Processing sentiment dynamically for each request increased latency and computational load.
Parsing complexities: Google’s evolving HTML structures required deep parsing, making long-term stability a concern.

Impact & Legacy

This project marked an early exploration into AI-driven sentiment analysis, demonstrating how natural language processing (NLP) could be used to assess and categorize public sentiment in real time. By integrating SentiWord’s advanced linguistic modeling, Riot of Colors showcased the potential of AI to provide deeper contextual understanding beyond simple keyword-based sentiment analysis.

The insights gained from this prototype influenced future developments in AI-powered content analysis, automated sentiment tracking, and real-time data interpretation. The project also highlighted key challenges in AI implementation, such as data accuracy, contextual nuance, and processing efficiency, which later informed more scalable and adaptable AI-driven solutions.

Additionally, this work laid the foundation for integrating machine learning into brand monitoring, media analysis, and digital engagement strategies, paving the way for businesses and organizations to harness AI for real-time consumer insights, trend forecasting, and automated reporting. Riot of Colors’ early investment in sentiment analysis positioned the agency as a forward-thinking innovator in AI applications for digital strategy and user experience.

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