Social media data have been proved to be effective in augmenting stock price forecasting models before ([8], [12]), but given the intrinsic speculative nature of traders who may use these innovative datasets, it appears more reasonable to investigate the relation between the Twitter data and the stock option prices. The underlying hypothesis is indeed that speculative trading strategies as the ones based on social media inference are may be more effective if evaluated on speculative instruments instead of simple stock prices. Consistent with previous works, it has been then studied on an intraday basis for three major technology stocks over a two-month period the relation between investors’ sentiment and basic financial products. A set of different variables has been created to include different interactions between sentiment and option prices, and a statistical selection model has been put in charge of identifying the most relevant correlations. The results are quite mixed: social media data seem to be indeed useful for predicting some option prices, but no others, and in particular are able to better explain single companies’ option price oscillations rather than the ones related to general indexes such as the Nasdaq-100.