Speculators often make use of mathematical and algorithmic extractions of previous events within their specific markets of interest in order to ascertain future outcomes. This is born out of the natural tendencies of humans to respond in the same manner under similar circumstances. Hence, the essence of chart analysis and predictive indicators.
Basic Analytical Processes
When making analysis, any given company, investor or speculator examines the underlying forces that affect the well being of the economy, industry groups and other indices that will affect the profit making opportunities for a given time space in the future. However, for companies that deliver products or specific services, they seek effective methods that would afford them a clearer picture of public perception and demands. This will eventually determine their focus and approach towards providing such services and product delivery.
Being able to extract this information effectively has for a long time remained the reserve of just a few big companies. These companies have the capacity to acquire and process big data, which usually requires a lot of commitment in terms of funds and other implications. This leaves majority of the private sectors with the options of guessing, or making predictions based on limited information.
This traditional practice of analysing existing data in order to extract a repetitive pattern that could offer direction is the motivation for Senno’s more precise creation that functions based on ‘Sentiment Analysis’. This system refers to the use of natural language processing, text analysis, computational linguistics, and biometrics to systematically identify, extract, quantify, and study affective states and subjective information.
Sentiment over History
Rather than studying charts and the end product of people’s actions, the process adopted by Senno makes use of ongoing real-time opinions and sentiments, rather than just past behavioural patterns. In other words, this system focuses on the expectations of a given community in predicting future outcomes. This practice is widely applied to voice of the customer materials such as reviews and survey responses, online and social media, and healthcare materials for applications that range from marketing to customer service to clinical medicine.
Generally speaking, sentiment analysis aims to determine the attitude of a speaker, writer, or other subject with respect to some topic or the overall contextual polarity or emotional reaction to a document, interaction, or event. The attitude may be a judgment or evaluation, affective state (that is to say, the emotional state of the author or speaker), or the intended emotional communication (that is to say, the emotional effect intended by the author or interlocutor).
Big Data for Everyone
By building on the NEO platform with an open SDK that uses distributed hardware, Senno will allow third parties to integrate sentiment and data conclusion tools into their own platforms via its API. This will enable companies and individuals to tap into and stay connected to sentiment by getting real time indications of the public opinion on a specific entity in any field.
Several advantages exist within this new system over the traditional methods, and these advantages would help companies and individuals ascertain the pulse of their respective ecosystems in real time, thereby making more informed judgements.
As already mentioned above, sentiment analysis via big data is not particularly new in the industry, rather the cost implication involved over the years has made it an exclusive reserve for just a few companies. Therefore, the blockchain implementation by Senno is aimed at decentralizing this particular trade, making is much more affordable to the general public.
Corporate and independent traders, investors and smaller companies are now provided with the tools to properly identify trends, threats, opportunities e.t.c. This creation will indeed enable a major improvement in the quality of analysis and subsequent forecasts and market predictions going forward.