Strategy #0000000061 · Text classification and market sentiment
Social text sentiment with Bernoulli Naive Bayes
Use binary features to illustrate Bernoulli Naive Bayes, exposing the prior, conditional likelihoods and decision threshold.
Bernoulli Naive Bayes represents each word or text feature as present or absent, then combines class priors and conditional likelihoods. It is simple and interpretable, but semantics, sarcasm, bots, reposts, timestamps and sampling affect the signal. The posterior is a text-class score, not a market-return probability.
A text-class posterior is not the probability of a price rise; features are assumed conditionally independent, and social data are noisy.
Three ways in
Understand the story. Then inspect the risk.
Start with a familiar situation, check the payoff, then test how the numbers change.
The idea · in two minutes
How do observed text features update the posterior score for an up class?
Ava encodes two toy text features as present or absent and enters their historical frequencies in up- and down-class text.
The simulator updates the up-class score with Bayes’ rule. It only takes a hypothetical position beyond the selected threshold, then applies a manually entered market return.
The example assumes features are conditionally independent and ignores language context and event-time alignment. It reads no social data and has no labelled corpus, calibration or out-of-sample results.
- Posterior up-class score from two present featuresEnter the up-class prior, two feature flags and class-conditional rates to inspect posterior classification and a hypothetical trade.
- Assumed long-signal return after costsA binary teaching example with no social-platform connection; omits spam, context, availability time, label error and calibration.
- Market data and forecast calibrationIllustrative expiry outcome.
Illustrative research examples only. They are not live signals or a recommendation.
The mechanics · payoff and exposure
P(U\mid x)\propto P(U)\prod_j P(x_j\mid U),\quad P(D\mid x)\propto P(D)\prod_j P(x_j\mid D)
U and D are text classes; x_j marks feature presence. Naive Bayes assumes conditional independence given the class.
Greeks: the directional intuition
- 資料治理
- Measures sensitivity to implied volatility.
- 時間對齊
- Measures sensitivity to implied volatility.
- 語境與漂移
- Measures sensitivity to implied volatility.
P(U\mid x)\propto P(U)\prod_j P(x_j\mid U),\quad P(D\mid x)\propto P(D)\prod_j P(x_j\mid D)
Practical context · published observations
Social text sentiment with Bernoulli Naive Bayes: read the record with its caveats.
The chart uses the published figures or explicitly labelled study inputs below. A historical index or model replay is not the same as an investor’s realised account return.
二元教學例、不連接社群平台；不含垃圾訊息、語言語境、資料可用時間、標註誤差或模型校準。 Source ↗ · Methodology ↗
Change assumptions and inspect the scenario
Inputs illustrate the strategy mechanics; they are not live quotes, forecasts or investment advice.
Variants · same family, different risk levers
Related methods still carry different exposures and failure modes.
Compare the input, position and assumptions that can fail.
| Related strategy | Relationship | What changes | New cost or risk |
|---|---|---|---|
| Bag-of-words sentiment | Token representation | Builds text features from word presence or counts. | Ignores context and word order. |
| FinBERT sentiment | Financial-language embedding | Uses a finance-trained language model for context. | Label bias, versioning and inference latency. |
| Contrarian social sentiment | Crowd sentiment extremes | Tests contrarian exposure at sentiment extremes. | Sentiment extremes can persist. |
A binary teaching example with no social-platform connection; omits spam, context, availability time, label error and calibration.
Read beyond the summary
References
Primary sources and research used to describe the strategy and its limits.