Introduction


Sentiment analysis is the interpretation and classification of emotions (positive, negative, and neutral) within text data using text analysis and natural language processing techniques.


Why perform SENTIMENT ANALYSIS?

  • Understanding the emotions & attitude behind the feedback shared across the teams and survey responses is essential for Organizations.
  • 80% of data are unstructured and in high volumes
  • It’s hard to understand, analyze & act upon
  • Managers understand how the feedback sounds like while writing feedback to the team members


Example


FEEDBACKSENTIMENT
“You are sometimes result-oriented. But more often the objectives are not met”
NEGATIVE
“You are sometimes result-oriented. You can become better if you set clear objectives for your team members. Also please keep an eye on the performance dashboard for better tracking of Objectives” NEUTRAL
“You are sometimes result-oriented. You can become better if you set clear objectives for your team members. Also please keep an eye on the performance dashboard for better tracking of Objectives. These corrective actions will bring very good success for you.”POSITIVE 


How does SENTIMENT ANALYSIS work?


Sentiment analysis inspects the given feedback and identifies the prevailing emotional opinion within the text, especially to determine a writer's attitude as positive, negative, or neutral.


Score of the sentiment ranges between -1.0 (negative) and 1.0 (positive) and corresponds to the overall emotional learning of the text.

Magnitude indicates the overall strength of emotion (both positive and negative) within the given text, between 0.0 and +inf. Each expression of emotion within the text (both positive and negative) contributes to the text's magnitude (so longer text blocks may have greater magnitudes)