Toronto Transit Sentiment Analysis

Understanding Public Opinion Through AI-Powered Text Analysis

Key Insights at a Glance

Overall Sentiment
Mixed
41% Neutral, 32% Positive, 27% Negative
Most Discussed Topic
Delays
38% of conversations
Model Accuracy
88.7%
Using BERT AI Model

Sentiment Distribution

What This Means:

Most riders have a neutral experience - the service works as expected
Positive feedback (32%) slightly exceeds negative feedback (27%)
Focus areas: Address the 27% negative sentiment to improve satisfaction

Main Discussion Topics

1. Delays & Reliability (38%)

Riders are most concerned about service delays and on-time performance.

delays late waiting schedule
2. Cleanliness (22%)

Vehicle and station cleanliness is a significant discussion point.

clean dirty smell maintenance
3. Safety & Security (18%)

Passenger safety concerns, especially during late hours.

safe security crowded concerns
4. Fare Pricing (12%)

Discussions about fare costs and value for money.

fare price presto expensive

Sentiment by Route Type

Key Findings:

Subway lines receive more positive feedback than streetcars and buses
Streetcars have the highest complaint rate, mainly about delays
Weekend service receives higher satisfaction scores than weekday rush hour

How This Analysis Works

Our AI-powered sentiment analysis uses advanced Natural Language Processing (NLP) to understand public opinion:

  1. Data Collection: We analyze thousands of social media posts, reviews, and customer feedback about TTC services.
  2. Sentiment Classification: AI models (BERT, VADER) automatically classify each message as Positive, Neutral, or Negative.
  3. Topic Extraction: Machine learning identifies common themes like delays, cleanliness, and safety.
  4. Insights Generation: Results are aggregated to reveal trends and actionable insights.

Business Recommendations

Priority Actions:

Improve Reliability: Focus on reducing delays, the #1 concern (38% of discussions)
Enhance Cleanliness: Increase cleaning frequency for vehicles and stations (22% of feedback)
Boost Safety: Increase security presence during late hours (18% of concerns)
Communication: Provide better real-time updates during delays to reduce frustration
Value Perception: Highlight service improvements to address fare concerns (12% of discussions)