The last few months, following summer, I have had a strong hunch that the prospects of sentiment analysis in general look very bright. Why? Because the marriage between semantic methods in linguistics and scientific methods from statistics and computer science is very timely and is starting to pay dividends.
With social media becoming more and more an integrated part of people’s lives and awareness, the question is no longer how to obtain information in text, but how to select the relevant parts of it. The information itself is out there readily available.
I have always felt that the core of our prediction method is to extract some entity or number connected to the mood or sentiment in texts and process it according to the needs of a certain issue or costumer.
One could compare the vision (haha) of text analysis to the fitting of a pair of glasses. Each eye needs an individually tailored lens in order to get a perfect impression of the outside world. If the lens is too weak or strong, this impression will be blurred to a smaller or greater extent. But there exists this optimal fitting that permits the viewer to experience a razor sharp picture of the surrounding environment.
The role of a practitioner of our trade is that of the optician’s. In an environment with an abundance of text available, information can easily be blurred by the sheer volumes of sources available. With our technology there is an emerge of tools to obtain relevant information as well as performing a ”read between the lines” analysis. And, as in the case with the glasses, what relevant information constitutes is highly individual and any tool provided by us must therefore contain the possibility to be fine tuned according to the needs of the user.
Enough said about visions. Lately we have been investigating the use of text analysis in risk management. Some exciting results have emerged both in terms of the interpretation of events but more importantly in the predictive power in foreseeing future changes in entities that have text written about them.
Recently, we have been using our sentiment analysis with other techniques in order to predict future credit ratings of companies so that now not only pure economical data is used for such an analysis, but also the information contained in the text in numerous articles about that very company.
The results have been very encouraging, displaying a connection between the average sentiment in articles and actual subsequent credit rating. A perfect example of our aim to link information contained in writing to a real quantity. We hope to see many opportunities of practical importance in the wake of these findings in the near future.