Ways Of Structuring Unstructured Data

Table of Contents

????? Overview of UNSTRUCTURED DATA

????? Examples showing challenges in analyzing unstructured data:

????? Approach for DATA ANALYSIS:

????? 1. Content Analysis:

????? 2. Sentiment Analysis:

????? 3. Frame Analysis:

????? 4. Discourse Analysis:

????? 5. Visual Content Analysis:

????? Conclusion:

?Overview of UNSTRUCTURED DATA

One of the biggest challenges as a data analyst today is analyzing and structuring Big Data. As we know about 80% of Big Data is unstructured.

When we say unstructured data, it means data that cannot be easily formatted to be stored in a table like SQL or an Excel sheet.

Some examples of unstructured data are images, audio, video, and text messages posted on social media. Every day, 1000 terabytes of data are stored online from around the world.

For example, approximately 500 terabytes of data are submitted a day to Facebook alone.

Examples showing challenges in analyzing unstructured data:

This data is very large and even if we consider only textual data, it is very difficult to analyze, because a sentence expressed by a person can have different meanings depending on the circumstances.

Let's look at some examples:

Sentence: “I need some space.”

? Context 1: In a crowded room, this sentence might mean that someone physically needs more room to move around.

? Context 2: In a romantic relationship, this sentence might mean that one person wants some personal space or time alone to think or relax.

? Context 3: In a conversation about computer storage, this sentence could mean someone needs additional storage space on their device.

?Sentence: “It’s cold in here.”

? Context 1: If someone says this while shivering in a room with the air conditioning set very low, they mean the temperature is uncomfortably cold.

? Context 2: If the same sentence is said when it’s winter outside, it might simply mean that the room is at a typical indoor temperature.

? Context 3: In a discussion about art, someone might say this to describe the color tone or atmosphere of a painting, not the actual temperature.

?These examples illustrate how the same sentence can take on different meanings depending on the situation or context in which it’s used.

?Approaches to Data Analysis:

As we see it makes it very challenging to analyze unstructured data. Let us see some ways which can be used to achieve this:

1. Content Analysis:

Content analysis is a method used to carefully examine written and recorded communication. Initially, it was about counting and measuring things in the text, like words and themes.

Some types of content analysis are

? Word Count: Count the number of occurrences of a given word in the sentence under analysis.

? ?Conceptual Content Analysis: In this method, we look for specific concepts or themes.

? Relational Analysis: In this method, we need to look for meaningful connections between sentences and ideas.

? Referential Analysis: This method considers things like background information, emphasis, and silence in the text to understand its complexity and meaning.

2. Sentiment Analysis:

Sentiment Analysis is the field of CRM that uses NLP and Machine Learning, to give computers an ability to understand the emotions expressed in the text message or post written by a user.

3. Frame Analysis:

Frame analysis is a method to examine a given scenario based on its frame. It looks at the perspective or context in which social information is presented to shape public opinion or understanding.

4. Discourse Analysis:

Discourse analysis goes beyond and above words and sentence analysis. It does not rely on just finding the meaning of a word and or county the frequency of the word in a sentence, but instead, it goes deeper. It is a qualitative analysis method that looks at the subject and its underlying meaning of a language in written or spoken form, within the context in which it occurs.

This method uses a language’s Social, Cultural, Political, and Historical background to interpret the meaning of a sentence.

?5. Visual Content Analysis:

While the text is the primary focus of most content analysis, visual content analysis involves analyzing images, videos, and other visual elements to extract meaning, themes, or patterns.

Conclusion:

In conclusion, we can say that one given sentence can draw different conclusions based on the circumstances, the relationship between the speakers, and the subject of discussion and culture. Hence a simple way of interpreting a sentence and building code around it is not enough.

We need to write advanced algorithms to use all of the above ways of analyzing unstructured data and iterating through it giving it a refinement every time, this can be done by a data analyst.

Building an AI model using machine learning is not a task that can be done in weeks, it requires technical expertise as well as a lot of effort to build algorithms, prepare pre-trained data, train machines, analyze the output of the algorithm, re-tune the algorithm if needed, and repeat.