Sentiment and Status Quo Test

It is the system that does not look at whether words are individually positive or negative, but what the action in the content changes compared to the situation yesterday.

Sentiment analysis does not look at word tones; it gives a negative decision if there is a new harm, positive if a harm is ending, and neutral (status quo) if the wheel turns and there is no clear result. For this reason, contrasting situations containing the same words are successfully separated.

Example: The title ‘A theft occurred’ separating as negative, while the title ‘The thief was caught’ separating as positive.

Go deeper

If the primary ML model fails to load, the rule engine kicks in for sentiment detection. This backup system performs dictionary scoring between -3 and +3 (342 negative, 284 positive words for TR). When it sees contrast conjunctions, it multiplies the weight before it by ×0.4 and the weight after it by ×0.6. While scoring, the density divisor is taken as ‘max(word count, 5)’ and the decision threshold is around ±0.25. However, the system cannot measure irony or a hidden bias (neutrality) within the context.