Concept-Level Sentiment Analytics

Technology Overview


Users do not need to change their OS, UI or IDE: our APIs are easy to use and to embed in any framework. Our company offer fine-grained solutions to many subtasks of sentiment analysis, e.g., polarity detection, aspect extraction, subjectivity detection, temporal tagging, named-entity recognition, concept extraction, personality recognition, and sarcasm detection, and they are available in different domains, modalities, and languages.


We show you what data is collected and how each of them is classified. Most companies, instead, adopt a black-box strategy in which they only show you the classification results. This way users can never be sure about how accurate their analysis really is because they usually do not disclose neither the data nor the techniques adopted for classifying such data (which, in most cases, are rather obsolete).


NLP research is evolving very fast, and the only way to be up-to-date with it is to be fully immersed in academia. We are not just a business company but also a research lab. We know the current and future trends of NLP, and we always embed the latest techniques in our APIs. Unlike most companies (which tend to focus only on one facet of the problem), we take a very multidisciplinary approach to sentiment analysis.

Technology Features & Specifications

Our company proposes an approach to NLP that is both top-down and bottom-up: top-down for the fact that it leverages on symbolic models such as semantic networks and conceptual dependency representations to encode meaning; bottom-up because we use sub-symbolic methods such as deep neural networks and multiple kernels learning to infer syntactic patterns from data. Coupling symbolic and sub-symbolic AI is key for stepping forward in the path from NLP to natural language understanding. Relying solely on machine learning, in fact, is useful to make a 'good guess' based on experience, because sub-symbolic methods only encode correlation and their decision-making process is merely probabilistic.

Potential Applications

Our company positions itself as a horizontal technology that serves as a back-end to many different business applications in the areas of e-business, e-commerce, e-governance, e-security, e-health, e-learning, e-tourism, e-mobility, e-entertainment, and more. Some examples of such applications include financial forecasting and healthcare quality assessment, community detection and social media marketing, human communication comprehension and dialogue systems.


Market Trends and Opportunities

In recent years, sentiment analysis has raised growing interest both within the scientific community, leading to many exciting open challenges, as well as in the business world. Due to the remarkable benefits to have financial and political forecasting, e-health and e-tourism, user profiling and community detection, manufacturing and supply chain applications, human communication comprehension and dialogue systems, etc.

Customer Benefits

Most companies offering sentiment analysis services today have very fancy websites or user interfaces but very poor algorithms behind them. Pretty much like a redecorated car with a fanciful body but an old engine. Our Artificial Intelligence (AI) engine, instead, represents state of the art in sentiment analysis research and allows our clients to have a real and accurate overview of what their customers like or dislike about their products and services.

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