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10 OCT

Technology Overview

SenticNet

SenticNet is a Singapore-based B2B company offering state-of-the-art sentiment analysis services. It differs from all other sentiment analysis providers for three main reasons: 1. SOLUTIONS SenticNet’s APIs are easy to use and to embed in any framework: users do not need to change their OS,...

SenticNet.png

Technology Overview

SenticNet is a Singapore-based B2B company offering state-of-the-art sentiment analysis services. It differs from all other sentiment analysis providers for three main reasons: 1. SOLUTIONS SenticNet’s APIs are easy to use and to embed in any framework: users do not need to change their OS, UI or IDE. SenticNet offers fine-grained solutions to many subtasks of sentiment analysis, e.g., polarity detection, aspect extraction, subjectivity detection, named-entity recognition, personality recognition, and sarcasm detection. All these are available in different domains, modalities, and languages. 2. TRANSPARENCY SenticNet shows users what data are collected and how each of them is classified. Most companies, instead, adopt a black-box strategy in which they only show users the classification results. This way, users can never be sure about how accurate the provided analysis really is because nor the data neither the techniques adopted for classifying such data are usually disclosed. 3. APPROACH Natural language processing (NLP) research is evolving very fast and the only way to be up-to-date with it is to be fully immersed in academia. SenticNet is not just a business company: it is a research lab. At SenticNet, we know the current and future trends of NLP and embed the latest techniques in our APIs, including both symbolic and subsymbolic AI algorithms.


Technology Features & Specifications

With the recent developments of deep learning, AI research has gained new vigor and prominence. However, machine learning still faces three big challenges:

(1) it requires a lot of training data and is domain-dependent;

(2) different types of training or parameter tweaking leads to inconsistent results;

(3) the use of black-box algorithms makes the reasoning process uninterpretable. At SenticNet, we address such issues in the context of NLP via sentic computing, a multidisciplinary approach that aims to bridge the gap between statistical NLP and the many other disciplines necessary for understanding human language such as linguistics, commonsense reasoning, and affective computing. Sentic computing is both top-down and bottom-up: top-down because it leverages symbolic models such as semantic networks and conceptual dependency representations to encode meaning; bottom-up because it uses subsymbolic methods such as deep neural networks and multiple kernel learning to infer syntactic patterns from data.


Potential Applications

SenticNet 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 become increasingly popular for processing social media data on online communities, blogs, wikis, microblogging platforms, and other online collaborative media. 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 be had from 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 fancy body but an old engine. SenticNet’s AI engine, instead, represents the state of the art in sentiment analysis research and allows their clients to have a real and accurate overview of what their customers like or dislike about their products and services.