SenticNet: Tapping Machine Learning and Natural Language Processing for Enhanced Market Sentiment Analysis

Opportunity

The sentiment analysis market is experiencing significant growth, driven by the increasing demand for customer feedback and opinion mining across various sectors. Businesses and organisations are leveraging sentiment analysis technologies to gain insights into customer perceptions, market trends, and to enhance their decision-making processes. SenticNet utilises machine learning and natural language processing, and is particularly valuable in social media monitoring, brand reputation management and market research. The rise of big data and the expansion of digital platforms have further fueled this growth.

As companies continue to focus on customer experience and feedback-driven strategies, the sentiment analysis market presents substantial opportunities for innovation, especially in developing more sophisticated analytical tools and applications for different languages and contexts. The integration of sentiment analysis with other AI technologies also opens up new avenues for advanced analytics solutions.

Technology

SenticNet’s APIs are easy to use and 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.

SenticNet shows users what data are collected and how each of them is classified. In contrast, most companies currently 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 neither the data nor the techniques adopted for classifying such data are usually disclosed.

Figure 1: The SenticNet framework consists of four key components: SenticNet, the knowledge base of affective common-sense, a semantic parser, for deconstructing text into natural language concepts, an extreme learning machine (ELM) based classifier that classifies concepts according to an emotion categorisation model, and sentic patterns, a set of linguistic patterns for polarity detection. 

Applications & Advantages

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 clients to have a real and accurate overview of what their customers like or dislike about their products and services.