1. A method of predicting an increase in sales from a plurality of online public discussions, comprising: receiving from a communications network a product information input for defining a product for which sales are predicted; receiving from the communications network a temporally defined input based on the number of times the product receives a mention in one or more sources of online chatter, wherein the temporally defined input comprises a mentions time series for the product derived from online chatter; generating by software installed on a computer host server a restriction that is automatically altered using a plurality of levels of disambiguation employing queries based on domain-specific keywords from the product information input to be applied to the temporally defined input; filtering with the software installed on the computer host server the temporally defined input with the restriction; generating by the software installed on the computer host server, a time-stamped temporally defined input from the filtered temporally defined input; generating by the software installed on the computer host server a signal quantifying the number of times the product is mentioned, wherein an amplitude of the signal is based on the number of times the product is mentioned between any two time-stamped temporally defined inputs; identifying a spike in the number of times the product is mentioned being present in the signal, wherein the spike is based on a rate of change in the amplitude exceeding a threshold value between the any two time-stamped temporally defined inputs, wherein the threshold value is based on a rate over time at which the product receives mentions from the plurality of online discussions; predicting with a processor a correlation value to create a comparison between the mentions time series and a sales rank time series of said product, wherein the sales rank time series represents measured sales of the product; where the correlation value is given by: r xy ⁡ ( k ) = c xy ⁡ ( k ) c xx ⁡ ( 0 ) · c yy ⁡ ( 0 ) where c xy is given by: c xy = 1 n ⁢ ∑ t = 1 n - k ⁢ ( x i - μ ⁡ ( x ) ) ⁢ ( y i - μ ⁡ ( y ) ) ⁢ k = 0 , … ⁢ , n - 1 , ⁢ c xy = 1 n ⁢ ∑ t = 1 - k n ⁢ ( x i - μ ⁡ ( x ) ) ⁢ ( y i - μ ⁡ ( y ) ) ⁢ k = - 1 , … ⁢ , - ( n - 1 ) , and where μ is a sample mean and k is a lag value and where c xx (0) and c yy (0) are sample variances of the sales rank time series and the mentions time series, respectively; determining an optimum lag value for values of k where the correlation value is a maximum, wherein negative values for the optimum lag value are considered leading and non-negative values for the optimum lag value are considered trailing; predicting the increase in sales of the product from the identified spike of the signal if the correlation value is at least 0.5 and the optimum lag is leading; and adjusting the levels of disambiguation if either the optimum lag is not leading or the correlation value is less than 0.5.