Statistical Foundations for a Google Play User-Review Sentiment Index: Signal Fusion, Shrinkage, Distributional Validation, and Dynamic Smoothing
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Google Play 用户评论情绪指数的统计基础：信号融合、收缩、分布验证与动态平滑
- 研究问题
- 如何判断既定采集协议下评论者的情绪是否发生值得调查的变化，并区分固定评论集合与协议定义人群的目标均值？
- 方法与思路
- 作者以序数阈值模型表示星级，在验证条件支持时采用双信号广义最小二乘近似，并将收缩、诊断和平滑分为独立模块。
- 材料陈述的贡献
- 摘要提出可审计的估计目标与模块接口；合成测试显示部分场景融合改善误差，协同污染导致严重偏差及区间失效。
- 阅读关注点
- 应核对采集协议、两类估计目标、条件无偏性的留出验证、权重改变目标的方式，以及敏感性分析与拒绝输出规则。
- 阅读边界
- 仅据 arXiv v2 预印本摘要，不能声称已同行评审或接收。作者未声称真实评论验证；所选评论不代表全部用户，合成表现不能直接外推。
原文摘要
The decision problem is whether valence among the users who write reviews of an app has changed enough to warrant investigation of a release, outage, or support issue. We propose a statistical specification for that decision, applied to written Google Play reviews collected under a declared locale, language, sort order, retrieval cap, and window rule. That endpoint returns a selected collection rather than a random sample, so the index describes review writers under the declared protocol and not all app users. What is new is not a sentiment classifier but an auditable interface: an ordinal threshold model is the preferred representation of star ratings, a calibrated two-signal generalized least-squares estimator is retained as an interpretable linear approximation when held-out labels support its conditional-unbiasedness assumption, the estimand is declared before the estimator, and every downstream module--uncertainty, diagnostics, and regularization--is tied to that declaration. The framework separates the latent average of a fixed collected set from the mean of a protocol-defined population of review writers, so measurement and between-review variation are not conflated, and treats helpfulness and recency weights as a different policy target. Shrinkage, cross-app empirical Bayes, histogram and dependence diagnostics, and dynamic smoothing are specified as separate modules with defaults and mandatory sensitivity analyses. A reference implementation, a declared 6000-review three-app collection, and a synthetic stress test exercise the displayed equations: fusion reduced error in clean and small-window scenarios, coordinated contamination caused severe bias and interval failure, and the collected sample reproduces the abstention rules. No real-review validation is claimed.