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How does sentiment analysis work in Social Listening on Sked?

Understanding Sentiment Analysis on Sked Social

Sked’s Social Listening includes built-in sentiment analysis to help you quickly understand how people feel about your brand, products, or competitors. Each mention is automatically classified as positive, neutral, negative, or mixed, helping you spot trends, prioritise engagement, and track brand health at scale.

 

How Sentiment Is Determined

We use language analysis tools, NLP (Natural Language Processing), to evaluate the tone and intent of each comment. It looks at word choice, phrases, and sentence structure—not just individual keywords—to understand the emotion behind what’s being said.

Sentiment Categories & Examples:

  • Positive: Expresses appreciation, enthusiasm, or approval

    “This is a fantastic update! Really appreciate the new features. 👍”
    “Your customer service is always so helpful and friendly. 😊”

  • Neutral: Factual, objective, or emotionally flat

    “Interesting. Tell me more about the pricing.”
    “This post was shared on my feed.”

  • Negative: Highlights frustration, disapproval, or dissatisfaction

    “I’m really disappointed with the recent changes. 😠”
    “The quality has really gone downhill. 👎”

  • Mixed: Combines both positive and negative sentiment in a single comment

    “I love the design, but the loading speed is really slow.”
    “Support was helpful, but it took a long time to get a resolution.”

Language Support

Sentiment analysis works across a range of major global languages, including:

  • English

  • Spanish

  • French

  • German

  • Italian

  • Portuguese

  • Japanese

  • Korean

  • Chinese (Simplified)

This allows Sked’s Social Listening to analyse sentiment accurately across a wide variety of audiences and regions.

Where Sentiment Analysis May Struggle

While sentiment analysis is powerful, language is nuanced. There are some situations where the system may not interpret tone accurately:

  • Sarcasm and irony

    “Oh, fantastic. Another update that breaks everything. 👍”
    Likely to be misread as positive.

  • Context-dependent messages

    “That’s just great.”
    May be interpreted as genuine unless seen in conversation.

  • Slang and emoji usage

    “This is fire! 🔥”
    Might be missed if the slang or emoji is unfamiliar.

  • Negation and complex phrasing

    “It’s not that I don’t like it, but it’s not what I expected.”
    Sentiment may be ambiguous or mixed.

  • Cultural variations

    What’s considered polite, blunt, or sarcastic can differ by region

 

Best Practices for Using Sentiment Data

Use sentiment insights to guide content strategy, reputation management, and customer engagement:

  • Spot trends early: Track sentiment shifts over time to understand content or campaign impact.

  • Prioritise your inbox: Address negative mentions first, and celebrate positive ones.

  • Adjust based on feedback: Use audience sentiment to refine tone, timing, and messaging.