Sentiment Analysis
Sentiment analysis is the use of natural language processing (NLP) to classify the emotional tone of text as positive, negative, or neutral. In social listening, sentiment analysis is applied to brand mentions to understand how people feel about a product, company, or topic — and whether that sentiment is improving or worsening over time.
How sentiment analysis works
Sentiment analysis models are trained on labeled text data. They assign a score (positive, negative, neutral — or more granular scales) to each piece of text. Modern models trained on social media data handle sarcasm and slang better than older rule-based systems, though edge cases remain a challenge for all approaches.
Sentiment analysis vs. intent scoring
Sentiment analysis tells you how someone feels about a topic. Intent scoring tells you whether that feeling represents a commercial opportunity. A negative sentiment post ("I hate my current tool") is far more valuable than a positive one ("I love my current tool") for lead generation purposes. WireTrap uses intent scoring rather than sentiment analysis for this reason.
Common questions about Sentiment Analysis
What is sentiment analysis in social media?
Sentiment analysis uses AI to classify social media posts as positive, negative, or neutral. It's used to track how a brand's reputation evolves over time and catch reputation crises early.
Does WireTrap use sentiment analysis?
WireTrap classifies intent (e.g. seeking recommendation, expressing frustration) rather than traditional positive/negative sentiment. Intent classification is more actionable for lead generation.
Related terms
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