Possible with Sentiment Analysis
It is the process of classifying whether a text is positive, negative, or neutral, which helps businesses make data-driven decisions
Gathering the Sentiment Signals
Collect diverse data sources - social media, customer reviews & surveys to capture the vast range of emotions & sentiments expressed by individuals
Breaking the Emotional Code
Train ML models - Naive Bayes, Support Vector Machines (SVM), or Recurrent Neural Networks (RNNs) to accurately classify sentiments in textual data
Real World Applications
Social Media Customer Service Marketing Sector Reviewer side
Challenges & Pitfalls
Ambiguity Data Bias Emotional Intensity Contextual Understanding