User:
Map your thoughts
Storyboard 
Our thoughts are often complex and interconnected, forming a true network.
This complexity and interrelation make it difficult for us to have an organized vision, often preventing us from gaining a clear understanding and reaching conclusions that encompass all relevant factors.
Therefore, it is useful to gather different thoughts through writing or conversation, which can later be organized and interconnected, creating a map of our thoughts that provides a clearer and more ordered perspective. This allows us to draw well-founded conclusions.
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The chaos of thinking
Description
If we take a moment to think and write down every thought on a post-it note (a small yellow piece of paper with an adhesive area), the result would probably be similar to the following wall:
ProjectManhattan, CC BY-SA 3.0
In order to gain an organic understanding of our thoughts, we could regroup the post-it notes by theme and then find ways to interconnect those that are related. This would involve reading each note, identifying key words, and associating them with other groups of post-it notes.
The task would be monumental, but the outcome could be useful in maturing our thoughts, realizing connections that we originally overlooked, and creating a concise and usable framework for decision-making.
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Analyze one thought at a time
Description
To start, we can take each thought (a post-it) one at a time and try to extract the key information it may contain:
Note: text writen in "Lorem Ipsum". "Lorem Ipsum" is a placeholder text commonly used in the design and typesetting industry. It is used as dummy text when the actual content is not available or when the focus is on the visual elements rather than the actual text.
Analyzing each thought, we realize that they are composed of one or multiple elements of information of the following types:
• Characteristics associated with an element.
• Actions between two elements.
Let's examine each of these elements and how they can appear multiple times within a single thought.
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Thought - Characteristic
Description
An example of information in the "characteristic" category could be:
which is composed of an
• element (apple) and
• adjective (red)
This can be represented graphically as:
The element can refer to objects, places, people (like Juan), and even the user (myself).
An additional aspect to consider is the level of objectivity or subjectivity that the adjective represents. For example, "the red apple" is an objective statement, while the statement "the apple is delicious" is a subjective perception.
Identifying the level of objectivity/subjectivity allows us to differentiate between facts (absolute, unchangeable) and perceptions (relative, potentially modifiable) when analyzing our thoughts.
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Thought - Actions
Description
An example of information in the "action" category could be:
which is composed of
• an entity (John),
• a verb (drives), and
• an entity (London)
This can be represented graphically as:
The entities are expressed through nouns and/or pronouns and can refer to objects, places, people (like Juan), or even the user (myself).
An additional aspect to consider is the polarity of the action, that is, whether it is undesirable (e.g., "steals"), neutral (e.g., "sells"), or desirable (e.g., "gives"). This also indicates the directionality, that is, which entity performs the action on which entity.
Identifying the polarity level allows us to recognize when a noun (person, place, object) receives or performs desirable or undesirable actions. This way, we can quickly identify threats and advantages associated with an entity.
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Thought - more complex
Description
An example of more complex information could be:
which is composed of two actions, where the second action implicitly refers to the first element. This can be represented graphically as:
In essence, every thought consists of:
• Components: characteristics and actions.
• Implicit references to the elements.
The second point highlights that we often don't explicitly write out every relationship between elements. Many times, we indirectly refer to an element, and the analysis of the text needs to identify such relationships.
By analyzing each thought and considering them collectively, we obtain a network of elements, their characteristics, and their relationships.
However, this network can be highly complex and needs to be analyzed in parts based on the specific focus of interest.
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Grid generation
Description
In the end, the text is analyzed phrase by phrase, identifying the elements, their characteristics, and their associations through the corresponding actions. All this information can be presented in a network that takes the form of:
Image of the module of this page with a medium-sized network representation corresponding to a text of approximately 500 characters.
For longer texts, this network can become quite complex. Therefore, the number of characters is limited to 5000 for subscribed users.
Due to server costs, traffic, and maintenance, the number of characters is limited to 500 for non-subscribed users.
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Network analysis
Description
As the network can become quite complex, it is useful to be able to filter certain elements and their surroundings. For this purpose, they can be selected and regenerated only with the part related to their characteristics and relationships with other elements:
Image of the page for analyzing the generated networks, where it is possible to select a group of elements and regenerate a subnetwork of their characteristics and related elements.
This exploration allows for summarizing all the characterizations of an element that are scattered throughout the text.
Similarly, the actions through which the element relates to all the other mentioned elements in the text can be analyzed.
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Network detail
Description
If you want to consult the details that originate from the analysis of the text, you can refer to the "Detail" tab:
Image of the page listing the details of the text analysis.
In this section, the keys (HKEY) and names (HEAD) of the elements that act upon the indicated elements are listed, along with their keys (LKEY) and names (LEMA). For the latter, the original "token" (TOKEN), classifications related to the word's role (TAG, POS, DEP) are indicated. Additionally, the values of polarity or subjectivity, gender, and color of the node are included. All these concepts are explained in detail under the description of how the analysis is performed and how the network is interpreted.
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