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Symbolic interactionism

What is symbolic interactionism?

Symbolic interactionism is a sociological theory that develops from practical considerations and alludes to particular effects of communication and interaction in people to make images and normal implications, for deduction and correspondence with others.

 In other words, it is a frame of reference to better understand how individuals interact with one another to create symbolic worlds, and in return, how these worlds shape individual behaviors. It is a framework that helps understand how society is preserved and created through repeated interactions between individuals. The interpretation process that occurs between interactions helps create and recreate meaning. 

It is the shared understanding and interpretations of meaning that affect the interaction between individuals. Individuals act on the premise of a shared understanding of meaning within their social context. Thus, interaction and behavior is framed through the shared meaning that objects and concepts have attached to them. From this view, people live in both natural and symbolic environments.

What is the symbolic interactionism theory?

Symbolic interaction is one of the several theories in the social sciences. According to this theory, people live both in the natural and the symbolic environment. Symbolic interaction is a process that is enlivened the reciprocal meaning and values by aid of the symbols in the mind. Meanings constitute of reciprocal interaction between persons. Objects don’t have meaning on their own. But objects get their meanings from the social actors. Consequently symbolic interaction is a process of “interpretation of the action.”

What is symbolic NLP?

Natural language processing (NLP) is the relationship between computers and human language. More specifically, natural language processing is the computer understanding, analysis, manipulation, and/or generation of natural language (according to dictionary.com).

Natural language refers to speech analysis in both audible speech, as well as text of a language. NLP systems capture meaning from an input of words (sentences, paragraphs, pages, etc.) in the form of a structured output (which varies greatly depending on the application). Natural language processing is a fundamental element of artificial intelligence.

Natural language processing, however, is more than just speech analysis. There are a variety of approaches for processing human language. 

The symbolic approach to natural language processing is based on human-developed rules and lexicons. In other words, the basis behind this approach is in generally accepted rules of speech within a given language which are materialized and recorded by linguistic experts for computer systems to follow.

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What is the difference between symbolic and statistical NLP processing?

Real NLP applications usually perform tasks using two families of approaches: symbolic and statistical. A symbolic approach consists on a set of rules, often hand-written but sometimes automatically learned that model different language phenomena. A statistical approach typically uses machine learning algorithms to learn language phenomena.

The statistical approach to natural language processing is based on observable and recurring examples of linguistic phenomena. Models based on statistics recognize recurring themes through mathematical analysis of large text corpora. By identifying trends in large samples of text the computer system can develop its own linguistic rules that it will use to analyze future input and/or the generation of language output.

What is symbolic system approach?

If one looks at the history of AI, the research field is divided into two camps – Symbolic & Non-symbolic AI that followed different path towards building an intelligent system. Symbolists firmly believed in developing an intelligent system based on rules and knowledge and whose actions were interpretable while the non-symbolic approach strived to build a computational system inspired by the human brain.

The traditional symbolic approach, introduced by Newell & Simon in 1976 describes AI as the development of models using symbolic manipulation. In AI applications, computers process symbols rather than numbers or letters. In the Symbolic approach, AI applications process strings of characters that represent real-world entities or concepts. Symbols can be arranged in structures such as lists, hierarchies, or networks and these structures show how symbols relate to each other. An early body of work in AI is purely focused on symbolic approaches with Symbolists pegged as the “prime movers of the field.”

What is the main idea of the symbolic interaction theory?

Symbolic interaction theory analyzes society by addressing the subjective meanings that people impose on objects, events, and behaviors. Subjective meanings are given primacy because it is believed that people behave based on what they believe and not just on what is objectively true. Thus, society is thought to be socially constructed through human interpretation. People interpret one another’s behavior, and it is these interpretations that form the social bond. These interpretations are called the “definition of the situation.”

For example, why would young people smoke cigarettes even when all objective medical evidence points to the dangers of doing so?1 The answer is in the definition of the situation that people create. Studies find that teenagers are well informed about the risks of tobacco, but they also think that smoking is cool, that they will be safe from harm, and that smoking projects a positive image to their peers. So, the symbolic meaning of smoking overrides the facts regarding smoking and risk.

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Symbolic interactionism

October 14, 2020

Table of contents

Key takeawaysCollaboration platforms are essential to the new way of workingEmployees prefer engati over emailEmployees play a growing part in software purchasing decisionsThe future of work is collaborativeMethodology

What is symbolic interactionism?

Symbolic interactionism is a sociological theory that develops from practical considerations and alludes to particular effects of communication and interaction in people to make images and normal implications, for deduction and correspondence with others.

 In other words, it is a frame of reference to better understand how individuals interact with one another to create symbolic worlds, and in return, how these worlds shape individual behaviors. It is a framework that helps understand how society is preserved and created through repeated interactions between individuals. The interpretation process that occurs between interactions helps create and recreate meaning. 

It is the shared understanding and interpretations of meaning that affect the interaction between individuals. Individuals act on the premise of a shared understanding of meaning within their social context. Thus, interaction and behavior is framed through the shared meaning that objects and concepts have attached to them. From this view, people live in both natural and symbolic environments.

What is the symbolic interactionism theory?

Symbolic interaction is one of the several theories in the social sciences. According to this theory, people live both in the natural and the symbolic environment. Symbolic interaction is a process that is enlivened the reciprocal meaning and values by aid of the symbols in the mind. Meanings constitute of reciprocal interaction between persons. Objects don’t have meaning on their own. But objects get their meanings from the social actors. Consequently symbolic interaction is a process of “interpretation of the action.”

What is symbolic NLP?

Natural language processing (NLP) is the relationship between computers and human language. More specifically, natural language processing is the computer understanding, analysis, manipulation, and/or generation of natural language (according to dictionary.com).

Natural language refers to speech analysis in both audible speech, as well as text of a language. NLP systems capture meaning from an input of words (sentences, paragraphs, pages, etc.) in the form of a structured output (which varies greatly depending on the application). Natural language processing is a fundamental element of artificial intelligence.

Natural language processing, however, is more than just speech analysis. There are a variety of approaches for processing human language. 

The symbolic approach to natural language processing is based on human-developed rules and lexicons. In other words, the basis behind this approach is in generally accepted rules of speech within a given language which are materialized and recorded by linguistic experts for computer systems to follow.

Build an AI chatbot to engage your always-on customers

What is the difference between symbolic and statistical NLP processing?

Real NLP applications usually perform tasks using two families of approaches: symbolic and statistical. A symbolic approach consists on a set of rules, often hand-written but sometimes automatically learned that model different language phenomena. A statistical approach typically uses machine learning algorithms to learn language phenomena.

The statistical approach to natural language processing is based on observable and recurring examples of linguistic phenomena. Models based on statistics recognize recurring themes through mathematical analysis of large text corpora. By identifying trends in large samples of text the computer system can develop its own linguistic rules that it will use to analyze future input and/or the generation of language output.

What is symbolic system approach?

If one looks at the history of AI, the research field is divided into two camps – Symbolic & Non-symbolic AI that followed different path towards building an intelligent system. Symbolists firmly believed in developing an intelligent system based on rules and knowledge and whose actions were interpretable while the non-symbolic approach strived to build a computational system inspired by the human brain.

The traditional symbolic approach, introduced by Newell & Simon in 1976 describes AI as the development of models using symbolic manipulation. In AI applications, computers process symbols rather than numbers or letters. In the Symbolic approach, AI applications process strings of characters that represent real-world entities or concepts. Symbols can be arranged in structures such as lists, hierarchies, or networks and these structures show how symbols relate to each other. An early body of work in AI is purely focused on symbolic approaches with Symbolists pegged as the “prime movers of the field.”

What is the main idea of the symbolic interaction theory?

Symbolic interaction theory analyzes society by addressing the subjective meanings that people impose on objects, events, and behaviors. Subjective meanings are given primacy because it is believed that people behave based on what they believe and not just on what is objectively true. Thus, society is thought to be socially constructed through human interpretation. People interpret one another’s behavior, and it is these interpretations that form the social bond. These interpretations are called the “definition of the situation.”

For example, why would young people smoke cigarettes even when all objective medical evidence points to the dangers of doing so?1 The answer is in the definition of the situation that people create. Studies find that teenagers are well informed about the risks of tobacco, but they also think that smoking is cool, that they will be safe from harm, and that smoking projects a positive image to their peers. So, the symbolic meaning of smoking overrides the facts regarding smoking and risk.

Thanks for reading! We hope you found this helpful.

Ready to level-up your business? Click here.

Let's build your first AI Chatbot today!


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