Method for analyzing data from the web characterized in that it comprises the steps of choosing a predetermined topic (S), said topic (S) identified by at least one keyword collecting data, or Web resources, from the Web that mention said predetermined topic (S) at successive instants t, two successive instants t being separated by an interval of time of predetermined length d counting the number W(S) of said Web resources that mention said predetermined topic (S) at each instant t generating a time-series of consecutive measures of the number of said Web resources, said time-series representing said number W(S) of Web resources as a function of time splitting said time-series into a plurality of consecutive time windows T of predetermined length z, with z≥d, in such a way that each time window T comprises at least one web resource among said web resources applying a correlation level quantifying technique to said plurality of time windows for quantifying, for at least one time window among said time windows, the level of correlations Lc existing in the Web resources W(S) of a same time window T estimating, for each time window T, the average number WM(S) of said Web resources W(S) that mention said topic (S) computing, for each time window T, a trend index by combining said average number of said Web resources WM(S) with said level of correlations Lc repeating said computing step of said trend index for all said time windows generating a sequence of trend indexes which show how opinions that the society has on a topic S changed over time.
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