How is the Reported Sentiment Score Calculated?

The reported sentiment score on Buzzlytix is a comprehensive metric that reflects the overall mood of news coverage across all analyzed articles and sources. Here's how we calculate it:

1. Article Collection & Preprocessing

• We gather articles from a variety of news sources daily.
• Each article is processed to extract its main content and title.

2. Sentiment Analysis

• For each article, we use a 5-class sentiment model (very positive, positive, neutral, negative, very negative) to predict the sentiment of the article's content.
• If the content is missing, a fallback 2-class model is used on the title.
• Each article's sentiment is represented as a probability distribution across the five classes.

3. Keyword Extraction & Consolidation

• We extract key entities (people, organizations, places, etc.) from each article's title using advanced natural language processing (spaCy).
• Keywords are consolidated to group similar or related terms together, ensuring accurate aggregation.

4. Aggregating Sentiment by Keyword

• For each keyword, we aggregate the sentiment distributions from all articles mentioning it.
• Soft counts (probabilities) are summed, and only keywords with at least 5 articles are included in the final statistics.

5. Calculating the Overall Sentiment Score

• For each keyword, we compute a sentiment score by assigning values to each class: very negative = -2, negative = -1, neutral = 0, positive = 1, very positive = 2.
• The score is normalized to the range [-1, 1] and damped by the proportion of neutral sentiment (to reduce the impact of uncertainty).
• The final reported sentiment score is the average of these damped scores across all keywords.