PR teams win by mastering two levers: sentiment analysis (decoding tone at scale) and share of voice (SOV) (knowing where you dominate the conversation). Together they turn raw coverage into strategy—catching risks early and amplifying what works.
Sentiment analysis is the AI-powered process of classifying opinions expressed in text as positive, negative, or neutral. It fits across the PR lifecycle: always-on media monitoring, early crisis detection, and post-campaign reporting. For example, after a product launch, teams isolate coverage that references onboarding speed. Tightening the “getting started” section in the press kit leads to a measurable lift in positive story framing the following week [9].
Industry reporting indicates most PR teams now prioritize early signal detection from sentiment trends to reduce time-to-response in issues management [9].
Delve in practice Many platforms stop at article-level polarity. Delve models sentiment at three layers—brand/company, tracked entities (products, executives, events), and message pull-through (your exact talking points)—to distinguish what is positive/negative and why.
Share of voice (SOV) is the percentage of total media coverage that mentions your brand compared with competitors. Here's the formula: Brand mentions ÷ Total mentions in category = SOV% [2]. Why overlay SOV with sentiment? Volume by itself can be a vanity metric; SOV paired with tonality reveals whether spikes are favorable or damaging [2,9]. Counting coverage without tone obscures risk and opportunity—overlay sentiment to understand quality of attention, not just quantity (synthesized from PR.co) [9]. As an example, if SOV jumps from 25% to 33% during a pricing news cycle but net sentiment turns negative, the spike likely reflects controversy, not momentum—redirect narrative and brief spokespeople accordingly.
Share of Mentions (SoM) compares the count of in-text brand references across all articles in a category, not just whether you were mentioned at least once. This captures how loudly your brand is named within coverage.
Delve calculates Share of Mentions by counting approved brand variants within each story and can break SoM down by entity (e.g., product vs. company) and by message (e.g., “instant setup”) to show which narratives drive prominence.
Key components include tokenization, part-of-speech tagging, and machine-learning classifiers; deep-learning models now support broad multilingual coverage (200+ languages reported by leading vendors) [7]. Both supervised and unsupervised approaches are used; for niche terminology, prefer tools that support custom model training on your annotated samples [3].
Why it matters One article can praise the product, critique pricing, and remain neutral on the CEO; entity/aspect views prevent misleading “overall” labels.
Case pattern In a multi-stakeholder incident, isolating negative sentiment tied to “security policy” around a specific executive clarifies the response track (policy comms vs. product patch).
Delve tracks products, executives, and events as entities and attaches message-level hits (your explicit claims) to reveal which proof points correlate with positive or negative tone.
Sarcasm detection remains imperfect; leading platforms combine contextual embeddings with emoji/marker libraries, but expert review is advisable for crisis coverage [9]. We recommend using a hybrid workflow: automated scoring for scale paired with human verification on high-stakes clips and major launches.
Ensure access to: national print, local/regional outlets, trade journals, TV/radio transcripts, online news, and major social platforms. Paywalled sources matter for complete earned-media value and executive-grade reporting [7].
Ask vendors for precision/recall benchmarks, vertical model options, and tonality breakdowns beyond polarity (e.g., trust, excitement, concern). Run a pilot on your historical data and edge cases before committing.
Look for alerts (Slack/Teams), CRM/BI/data-warehouse connectors, and exportable dashboards. Scrutinize hidden costs (API limits, historical backfill, analyst seats). Consider negotiating annual vs. monthly terms for budget efficiency.
Delve offers entity/message-aware sentiment outputs designed to feed BI dashboards and leadership readouts with SOV + SoM context.
Below is an at-a-glance look at seven platforms, grouped by signature strengths. (Vendor claims summarized from public materials; confirm the latest before purchase.)
| Language coverage | Signature strength | Pro/enterprise | |
|---|---|---|---|
| Talkwalker |
~127 languages [1] |
Predictive anomaly/crisis alerts |
Popular in CPG, telecom; topic clusters |
| Meltwater |
Up to 242 languages; ~218 sentiment models [7] |
Global multilingual monitoring |
Broadcast clipping; influencer add-ons |
| Cision Communication Cloud |
Broad, incl. paywalled; ~96 languages [7] |
Premium news sentiment + EMV |
Deep publisher access; executive reporting |
| Brandwatch Consumer Intelligence | Global social |
Social listening depth (Iris AI, React Score) |
Influencer segmentation, visualizations [1] |
| Lexalytics |
Enterprise, developer-friendly |
Custom/on-prem; aspect-based sentiment |
SDKs/APIs; favored for tailor-made models [5] |
| Brand24 | Multi-platform | Budget monitoring |
Lower price; fewer languages; no paywalled content |
| Awario | Multi-platform | Budget monitoring |
Similar trade-offs as Brand24 |
| Delve | Up to 242 languages |
Entity & message-aware sentiment with Share of Mentions (SoM) |
Earned-media focus; SOV + SoM side-by-side |
[1] BuildBetter. 10 Best AI-Powered Brand Sentiment Analysis Tools to Transform CX in 2025
[2] Sprout Social. Share of Voice: What It Is & How to Measure It
[3] SurveySensum. Sentiment Analysis Tool
[4] Cision. Insights: Share of Voice for PR
[5] Kapiche. Best Sentiment Analysis Tools
[6] Launchmetrics. Measure Earned Media Value
[7] Sprout Social. Sentiment Analysis Tools
[8] Prowly. PR Metrics: Share of Voice
[9] PR.co. The Future of Sentiment Analysis
[10] Pacvue. Ultimate Guide to Share of Voice in Retail Media
