Sentiment analysis

Sentiment analysis, also called opinion mining, classifies evaluative language in text and can be extended cautiously to audio or video. It is often used with Text Analytics, Video Analytics or Voice Analytics, but each medium presents different validity and ethical limits.

When to use it

Use sentiment analysis when a decision requires a consistent summary of expressed opinion across a volume of feedback.

It can help explore:

• How customers describe a brand or experience.
• Which product themes attract praise, criticism or uncertainty.
• How perceptions differ from competitors under comparable data.
• Which employee-experience topics require direct investigation.

Use it for triage and pattern detection, not as a covert measure of a person’s true emotional state, honesty, health or intent.

Origins

The field combines computational linguistics, information retrieval, text classification and earlier content analysis. “Opinion mining” and “sentiment analysis” became prominent as online reviews, forums and social media created large collections of evaluative text. Lexicon methods were followed by supervised machine learning and later contextual language models.

What it is

A basic model assigns text to positive, negative or neutral classes. More detailed systems identify target, aspect, intensity, stance, emotion or change over time. The unit matters: a review can praise delivery and criticise durability in the same sentence.

The claim that words account for only 7 per cent of communication is a misuse of Albert Mehrabian’s narrow experiments on inconsistent emotional messages. It is not a general law of comprehension and does not justify inferring truth from tone or body language.

Sentiment is expressed, contextual and culturally variable. Sarcasm, negation, dialect, mixed opinion, quoted speech and domain language create error. Aggregate results may also reflect who chose to speak, platform moderation and coordinated activity rather than the full stakeholder population.