Beyond Sentiment: The Value and Measurement of Consumer Certainty in Language
Bibliographic record
Abstract
Sentiment analysis has fundamentally changed marketers’ ability to assess consumer opinion. Indeed, the measurement of attitudes via natural language has influenced how marketing is practiced on a day-to-day basis. Yet recent findings suggest that sentiment analysis's current emphasis on measuring valence (i.e., positivity or negativity) can produce incomplete, inaccurate, and even misleading insights. Conceptually, the current work challenges sentiment analysis to move beyond valence. The authors identify the certainty or confidence of consumers’ sentiment as a particularly potent facet to assess. Empirically, they develop a new computational measure of certainty in language—the Certainty Lexicon—and validate its use with sentiment analysis. To construct and validate this measure, the authors use text from 11.6 million people who generated billions of words, millions of online reviews, and hundreds of thousands of entries in an online prediction market. Across social media data sets, in-lab experiments, and online reviews, the authors find that the Certainty Lexicon is more comprehensive, generalizable, and accurate in its measurement compared with other tools. The authors also demonstrate the value of measuring sentiment certainty for marketers: certainty predicted the real-world success of commercials where traditional sentiment analysis did not. The Certainty Lexicon is available at www.CertaintyLexicon.com .
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.081 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.009 | 0.014 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".