Multi-Axis Sentiment in GEO
Consensus definition
Sentiment analysis as a discipline distinguishes at least three measurement constructs that the social-listening tradition tends to collapse into a single polarity score. Polarity — the positive/neutral/negative axis — is the canonical target of opinion-mining research; Pang and Lee's foundational monograph treats it as one task inside a broader family that also includes opinion-holder identification, target extraction and stance assessment1. Stance detection is a parallel research tradition that asks whether a text is committed for, against or neutral about a specific target proposition, with stance treated as analytically separate from polarity; the SemEval-2016 shared task established the operational vocabulary used across subsequent work2. Hedging and epistemic modality form a third, older tradition in applied linguistics: Hyland's work on academic discourse operationalises hedging as a measurable feature distinct from polarity, marking the writer's commitment to the claim rather than its valence3; computational treatments — FactBank, the BioScope corpus, the CoNLL-2010 shared task — instrument hedge and speculation detection as a separate annotation layer over the same text45. Each of the three constructs is well-developed in its own literature. What marketing-analytics tooling typically ships is the first axis only, applied at the whole-document level, on text it was calibrated for: short opinionated user-generated content. Transferring that instrument unchanged to LLM answers — which are mostly hedged, informational and multi-perspective — is the methodological gap this entry addresses.
rhinegold operator refinement
Rhinegold proposes two structural choices that together make sentiment measurement in LLM contexts operationally useful: Statement-centric, not response-centric. The unit of measurement is the individual statement about the brand — a sentence or clause that asserts or implies something about it. A response that contains three statements about the brand produces three measurements, not one. This handles comparison answers natively (where assigning a single tone to a response that discusses multiple providers either collapses or falsely attributes), and it surfaces the cases where a single response carries both an assertion and a hedged qualifier — operationally common and methodologically informative. Multi-dimensional, not single-axis. At minimum three dimensions, scored independently: 1. Polarity — positive, neutral, negative: the classical axis. 2. Hedging grade — assertive, soft-hedged, strongly hedged: how committed the model is to the statement. 3. Confidence — high, medium, low: how the model frames the epistemic status of the claim ("X is" vs. "X appears to be" vs. "X may be"). Polarity and hedging are not the same axis. Confidence and hedging are not the same axis either — a model can be assertive about an uncertain claim ('there is no public data on Y's pricing'), or hedged about something it knows well. Conflating any pair of these axes loses information that is operationally relevant. The distribution of statements across the hedging axis is itself an indicator: a brand whose statements are mostly assertive — regardless of polarity — is being treated as a known entity; a brand whose statements are mostly hedged is being treated as underspecified, which is a different commercial problem from being treated negatively and calls for a different intervention. Self-disclosure. Rhinegold's own production GEO pipeline began with a response-level polarity model and replaced it with the statement-centric, multi-axis layer once it became clear that polarity alone moved very little across measurement waves — most of the operationally meaningful variation was happening on the hedging axis, which the single-axis instrument could not see.
Operational use
Segment each AI response into brand-relevant statements (sentence- or clause-level), and score each statement independently on three axes: polarity, hedging grade and confidence. Report the joint distribution, not just marginal means — a brand with a polarity mean near zero but 80 % assertive statements is in a very different position than a brand with the same polarity mean and 80 % hedged statements. Pair with AI Answer Sentiment as the conceptual parent and with Mention Rate so volume is always read with its sign and its epistemic register. On comparison prompts, the per-statement unit handles multi-provider answers without collapse. Hold scoring rubrics and the statement segmentation rule version-pinned across measurement periods: changing either silently changes the headline numbers.
Measurement boundary
Inter-rater agreement on hedging and confidence is imperfect even among trained annotators; the scores are qualitative adjustments to counting metrics, not precise numbers, and they are provider- and prompt-specific. Statement segmentation itself carries a rule-dependent error term — a long compound sentence may carry multiple stances. The three axes do not measure factual accuracy: a brand can be assertively and falsely characterised at once, which is Hallucination territory, not a sentiment win. The specific scoring thresholds ("assertive" vs. "soft-hedged") are operational calibration, not universal truths; they need to be documented and frozen across comparable measurement waves.
Distinct from
From AI Answer Sentiment: the conceptual parent — sentiment as the layer above presence; the multi-axis decomposition is a structural refinement of how that layer is instrumented, not a competing concept. From Sentiment Drift: the temporal derivative — drift asks how the axes move over time; the multi-axis layer is the snapshot that drift differentiates. From Mention Rate and Mention Intensity: counting metrics for presence and depth, which carry no tone; the multi-axis layer qualifies them. From Mention Quality: a related qualitative read that captures the framing of a mention without the explicit polarity/hedging/confidence decomposition. From Hallucination: truth versus tone — orthogonal dimensions.
Common mistakes
- Reporting a single polarity number per response on comparison prompts: either the score collapses to neutral or it is falsely attributed to every brand named — both outcomes are uninformative.
- Treating the 'neutral' bucket as a residual: in polarity-only scoring of LLM answers, neutral absorbs informative-descriptive text, hedged recommendations and multi-perspective comparisons indiscriminately — three very different operational outcomes lumped into one cell.
- Conflating hedging with polarity: 'is the leading provider' and 'could be considered a strong option' both register as positive on a polarity axis, while differing decisively on hedging — the operationally meaningful signal is in the hedge, not the valence.
- Conflating hedging with confidence: a model can be assertive about an uncertain claim or hedged about a well-known one; collapsing the two loses the signal each one carries independently.
- Optimising for 'more positive sentiment' as a KPI: the operational lever sits at the level of source material — the public corpus and the grounding sources retrieved at inference — not the answer text; treating the indicator as a target is the same error as treating brand mentions as a target.
- Using social-media sentiment tools unchanged on LLM output: they are calibrated for short, opinionated user-generated content, and they misread the measured, hedged register of an LLM recommendation.
Sources & deeper reading
- 1Pang, B. & Lee, L. (2008). "Opinion Mining and Sentiment Analysis." Foundations and Trends in Information Retrieval, 2(1–2), 1–135. Canonical monograph that situates polarity classification inside a broader opinion-mining family also covering target extraction, opinion-holder identification and stance.
- 2Mohammad, S. M., Kiritchenko, S., Sobhani, P., Zhu, X. & Cherry, C. (2016). "SemEval-2016 Task 6: Detecting Stance in Tweets." Proceedings of SemEval-2016, 31–41. Establishes the operational vocabulary for stance detection (FAVOR / AGAINST / NONE relative to a target proposition) as analytically distinct from polarity.
- 3Hyland, K. (1998). Hedging in Scientific Research Articles. John Benjamins. Foundational applied-linguistics treatment of hedging as a measurable discourse feature marking the writer's commitment to the claim — analytically separate from the claim's valence.
- 4Saurí, R. & Pustejovsky, J. (2009). "FactBank: A Corpus Annotated with Event Factuality." Language Resources and Evaluation, 43(3), 227–268. Operationalises epistemic modality and event factuality as a corpus-level annotation layer over the same text on which polarity is scored — confidence as an independent axis.