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AI Data Analytics: Sentiment, Trend, and Anomaly Detection
Automated tools let auditors examine vast volumes of information to surface actionable facts and even gauge human emotion. This episode of the ISACA Advanced in AI Audit (AAIA) exam prep series introduces data analytics for AI auditors and zooms in on sentiment analysis — covering what it measures, how rule-based and machine learning approaches differ, and how auditors can use these techniques to validate whether a deployment is actually working.
What this episode covers
- Data analytics as interrogation of data — asking precise questions to get factual, actionable answers at scale.
- How AI shifts the auditor’s role away from manual checking toward high-level strategy and complex project auditing.
- Sentiment analysis (opinion mining) — scanning large volumes of text to determine emotional tone.
- Polarity — the technical term for the positive, negative, or neutral classification of sentiment.
- Sentiment analysis as an audit validation tool for measuring whether a new deployment is actually helping stakeholders.
- Rule-based sentiment analysis — a simpler keyword-matching method.
- Machine learning sentiment analysis — a context-aware method that understands word order and meaning.
- The algorithms behind ML sentiment analysis — linear regression, Naive Bayes, support vector machines, and deep learning.
Watch the full episode above for the worked examples and detailed explanations of each concept.
Frequently Asked Questions
What is data analytics in the context of an AI audit?
Data analytics is the practice of asking precise questions of your data to get factual, actionable answers, which auditors sometimes call the interrogation of data. AI can automate the review of thousands of records in seconds, flagging anomalies instantly and freeing auditors from repetitive manual checks so they can focus on high-level strategy and complex project auditing.
What is sentiment analysis and what is polarity?
Sentiment analysis, also called opinion mining, is the process of scanning large volumes of text to determine the underlying emotional tone, classifying it as positive, negative, or neutral. This classification of emotional direction is technically referred to as polarity.
What is the difference between rule-based and machine learning sentiment analysis?
Rule-based sentiment analysis is the simpler method that hunts for specific keywords, scoring text positive for words like wonderful or fast and negative for words like broken or awful. Machine learning sentiment analysis is more sophisticated because it analyzes the words and the specific order in which they appear to understand context, correctly flagging a phrase like a total lack of improvement as negative.
Which algorithms power machine learning sentiment analysis?
Machine learning sentiment analysis relies on linear regression to track trends between variables, Naive Bayes to calculate the probability of an emotion based on past data, support vector machines to draw mathematical boundaries that sort data points, and deep learning, which uses artificial neural networks to mimic human brain processing.
How can auditors use sentiment analysis to validate an AI deployment?
If a company deploys a new AI tool for its customer service team, an auditor can run sentiment analysis on internal feedback surveys to gauge what stakeholders think. This provides concrete, data-driven evidence of whether the deployment is actually helping rather than relying on a few watercooler conversations.
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Reference: This article is based on concepts discussed in AI Data Analytics: Sentiment, Trend & Anomaly Detection.