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  • Writer's pictureReinhard Lindner

Sentiment analysis as a required element of AGI

Today, Artificial General Intelligence (AGI) is a theoretical concept that is still being developed. Artificial intelligence (AI), in its turn, has a narrow practice. AGI is believed to be a substitute for human thinking. It should be creative, have sensory perception, and fine motor skills, possess natural language understanding (NLP), and provide great navigation. However, there is a lot of work and many nuances that have to be met to make AGI become a reality.

Just now, according to Dr. Alan Thompson, humanity has developed AGI at 55%..

One of the crucial features that AGI has to develop is sentiment analysis. Human decision-making is irrational and mostly based on the emotional aspects of every situation. Therefore, AGI has to be able to read these emotions correctly and think as a normal human, correspondingly.


To understand what is hidden under the sentiment analysis, let’s describe it in detail. Learn more about AGI’s sentiment analysis with Sencury!


Sentiment Analysis Defined 

Sentiment analysis is a sub-field of Natural Language Processing (NLP). Also, it is a tool and a part of such an AI subset called machine learning (ML). With the help of NLP and ML, it is possible to analyze texts for polarity (positive, neutral, negative). The more you train the language model with emotional texts, the better it learns how to detect sentiment within these texts. Further, this model will require no human input.  

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Simply speaking, NLP and ML give computers the possibility to learn new tasks without being programmed beforehand. Sentiment analysis models can be trained to read emotions beyond definitions, that are based on the context. To add, they will even find sarcasm there, or misapplied words. Everything depends on the input and the machine’s ability to learn.

How does sentiment analysis work 

The sentiment analysis process has three distinctive steps:  

  • data collection  

  • feature extraction 

  • classification 

First, you collect data from various sources (e.g., social media, online reviews, news articles). Then this data undergoes feature extraction. You can automate this step with the help of Natural Language Processing (NLP) techniques. Third, the data is classified and used to generate insights about the sentiment of a given text. 


Despite helping improve public opinion, sentiment analysis still has some accuracy issues. For example,

  • subjectivity of language 

  • irony or sarcasm  

  • bias by the demographic groups 

Therefore, the analysis itself will be relevant to a certain group of people, it will be based on their location, way to express sentiment through irony or sarcasm, and specific platform of use. 


Types of Sentiment Analysis 

Fine-Grained 

It is the basic type of sentiment analysis. The current type of sentiment analysis determines the accuracy of polarity. There are the following polarity categories:

  • Extremely positive 

  • Positive 

  • Neutral 

  • Negative 

  • Very negative 

It is best to apply fine-grained sentiment analysis to reviews and ratings. Here, it will be the most helpful.  

Aspect-Oriented 

It also belongs to the basic type of sentiment analysis. Aspect-focused analysis determines the overall polarity of your customer evaluations on a deeper level. It helps determine the very components that people are discussing.


Emotion Recognition   

It is an advanced sentiment analysis. This type of analysis is self-explanatory. People possess all kinds of emotions: anger, happiness, sorrow, anxiety, frustration, panic, etc. To detect all of these emotions, systems should be based on lexicons of specific words denoting concrete emotions. To make this analysis easier and more effective, it is best to use the ML methods. Every person expresses emotions differently and just a lexicon might not be enough to recognize what the text is about in an emotional context. 


Intent Evaluation   

It is also an advanced type of sentiment analysis. Consumer intent should be accurately detected from the start. This may be beneficial for any business in terms of cost, time, and effort savings. Some consumers have no intention to buy products and with a careful intent analysis, this barrier can be eliminated. Intent analysis aims to determine what the customer’s purpose is to purchase products, and whether the customer has plans to purchase something while browsing.

Sentiment Analysis and AGI

AGI is the new AI technology, a certain development of machines that will be capable of reasoning and learning equally to humans. Essentially, this type of intelligence can understand abstract concepts and perform tasks with high-level thinking. AGI is prone to surpass humans in some areas. At least, that is the purpose of its development and that is what scientists intend to develop.

With the help of sentiment analysis, AGI will be able to: 

  • Provide audience insights 

  • Measure marketing campaign ROIs 

  • Drive proactive business solutions 

  • Augment great PR practices 

  • Support customer services 

However, the biggest challenge of sentiment analysis is to overcome human language subjectivity and have a large dataset that covers sentiment expressions on various topics. This way, AI will catch the tone of the text and differentiate it from bias, sarcasm, irony, demographic phenomena, etc. For AGI it appears to be crucial in context understanding. The other way, it will not learn how to think like humans or surpass humanity. So far, models have to be pre-trained with specific datasets of sarcasm and irony. And other emotions, of course.


Nowadays, AI performs both basic and advanced sentiment analysis. If the basic analysis only requires the determination of polarity, the advanced type can identify joy, anger, sadness, fear, sarcasm, irony, and humor. The first one is used for social media monitoring, customer feedback analysis, and brand reputation management. The latter, in turn, is used in market research, opinion mining, and customer sentiment analysis.


Sencury Offers Sentiment Analysis Services 

With our extra knowledge and competency in AI and ML consulting as well as engineering solutions, Sencury also offers sentiment analysis. With its help any business will be able to 

  • Seamlessly convert raw text data into AI training data 

  • Improve accuracy of model and team outputs 

  • Enhance end-user experiences 

  • Ensure security of data  

  • Global workforce of industry-specific SMEs 

  • Save valuable time and money 


Consider Sencury as your top AI and ML expert to turn to! Contact us now and let’s find your perfect business solution together! 

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