Sentiment analysis is the classification of content – a URL, an article, an AI response – on a positive, neutral, negative axis. For reputation work it serves as a tracking signal rather than a decision criterion, because the same content can register different sentiments depending on the model and the prompt. The practical use: each ranking URL on the priority SERPs gets a sentiment score during analysis, and the aggregate sentiment of page one is tracked monthly. Each AI engine response in AIQ™ gets scored per engine, with the trend over time more meaningful than any single snapshot. Sentiment movement is a leading indicator of stakeholder perception movement, particularly when combined with source-attribution data showing which sources are driving the picture. The failure mode is treating sentiment as a goal in itself rather than as a signal of underlying narrative state – the work is on the narrative, not on the score.

Monthly progress reports at Five Blocks follow a consistent structure tied to the goals set at the start of the engagement. The sections: SERP movement against priority queries with specific URL gains and losses, illustrated through IMPACT™ charts. AI narrative state across the eight engines AIQ™ monitors – sentiment trend, source attribution shifts, themes emerging or fading. Wikipedia activity including any Talk-page work filed or accepted and any edit notifications from WikiAlerts™. Peer benchmarks showing the client’s position relative to the named peer set across each layer. Work completed during the period – source-level interventions, content built, profile work, technical changes. Three to five prioritized recommendations for the coming period. A short list of wins worth noting and risks worth flagging. Visuals over text where useful. The report is the operational document for the engagement, not a marketing artifact.

Competitive reputation tracking is one of the higher-value outputs of a program because it converts the data into strategic action: where peers have advantages worth closing, where the client has advantages worth defending, where the category is shifting. IMPACT™ runs the client’s priority query set against named peers in identical geographies and languages, producing per-query SERP composition comparisons. AIQ™ runs the same prompts against each peer across the eight engines, producing model-by-model comparison of narrative state, source attribution, sentiment, and theme coverage. The aggregate view shows share of voice on each priority query and across the AI narrative as a whole. The output feeds two things: monthly reporting where peer comparison usually proves more actionable than absolute numbers, and strategic recommendations about which interventions to prioritize based on where the leverage is greatest relative to the competition.