In large part, the economy is shaped by words. The attitudes of investors, bankers, politicians, and the general public drive the market—a linguistic and monetary reality that has only become more apparent since the ascendency of the internet in global trade.
Because of this phenomenon, a methodology of systematic language interpretation was developed. This process, known as sentiment analysis, had several forms. Basic sentiment analysis used a fairly straightforward method: first, two separate dictionaries of positive and negative buzzwords were created; then, the sum total of the negative buzzwords in a given document was subtracted from the sum total of the positive words in that document; this simple mathematical operation produced the document’s score.
Including positive and negative phrases into their term dictionaries, the most sophisticated iterations of sentiment analysis bolstered accuracy by taking such complexity into account.
The advantages of such an automated process were numerous: programs could consume far more information far more quickly than a human analyst—or even a team of analysts—ever could; programs could produce evaluations nearly instantaneously; and, perhaps most importantly, programs did not fall prey to the various biases that plague human interpretation.
Yet for all its advantages, classic sentiment analysis could not reproduce the sophistication of human evaluation. To truly assess a piece of communication, an interpretation must account for far more than individual words and phrases: assertions develop over sentences, paragraphs, and the piece as a whole, and credible evaluations take such breadth into account.
In addition, these techniques often operated with dictionaries of words assembled by programmers—not analysts with domain expertise. Since the accuracy and inclusiveness of the term dictionaries is vital to the credibility of automated evaluations, the failure of programmers to incorporate domain expertise into the foundation of the automated process counted as a significant strike against classic sentiment analysis.
Such shortcomings have inspired the founders of Prattle to develop a new generation of sentiment analysis. Leveraging the inclusivity, speed, and objectivity of sentiment analysis while benefiting from the sophistication and depth of domain expertise, this propriety process puts the best of both under one hood.
We’ll begin to tackle this novel process in part 2 of this series.
The Prattle Team