6 Text Mining Techniques for Brand Image Analysis

The process of converting unstructured text into structured data for easy analysis is known as text mining (also known as text analysis). Text mining employs natural language processing (NLP), which enables machines to understand and process human language automatically.

 

The proliferation of online consumer reviews has resulted from the Internet's growth. So far, papers investigating online reviews have primarily examined how non-textual features, such as ratings and volume, influence various types of consumer behavior, such as information adoption decisions or product selection. 

 

However, there has been little attention paid to examining the textual aspects of online reviews to study brand image and brand positioning. The process of extracting useful and meaningful information from unstructured text is inextricably linked to text analysis of online reviews.

 

What is Text Mining?

 

Text mining is an automated process that extracts valuable insights from unstructured text using natural language processing. Text mining automates the process of classifying texts by sentiment, topic, and intent by transforming data into information that machines can understand.

 

Businesses can now analyze complex and large sets of data in a simple, fast, and effective manner thanks to text mining. Simultaneously, businesses are utilizing this powerful tool to reduce some of their manual and repetitive tasks, saving their teams valuable time and allowing customer support agents to focus on what they do best.

 

Text mining also referred to as text data mining is the process of converting unstructured text into a structured format to identify meaningful patterns and new insights. Companies can explore and discover hidden relationships within their unstructured data by using advanced analytical techniques such as Naive Bayes, Support Vector Machines (SVM), and other deep learning algorithms.

 

Text is one of the most commonly used data types in databases. Depending on the database, this information can be organized as follows:

 

1. Structured Data: is data that has been standardized into a tabular format with many rows and columns to make it easier to store and process for analysis and machine learning algorithms. Names, addresses, and phone numbers are examples of structured data inputs.

 

2. Unstructured Data: This data lacks a standard data format. It can contain text from sources such as social media or product reviews, as well as rich media formats such as video and audio files.

 

3. Semi-structured Data: is, as the name implies, a hybrid of structured and unstructured data formats. It has some organization, but not enough structure to meet the requirements of a relational database. XML, JSON, and HTML files are examples of semi-structured data.

 

What is Brand Image Analysis?

 

The customer's perception of your brand based on their interactions is referred to as a brand image. It can evolve and does not always involve a customer purchasing or using your product or service. Because customers' perceptions of your brand can vary, it's critical to work hard to maintain a consistent brand image.

 

Businesses invest a significant amount of time in developing a brand's personality, voice, and market positioning. It extends beyond visible elements such as having an identifying symbol, mark, or logo that businesses can use to differentiate themselves from competitors. All of these brand elements influence how a customer interacts with and perceives your brand, resulting in brand image

 

Many consumers today (particularly millennials and Generation Z) buy your product or service not because it is the best option, but because of what you stand for.

 

In fact, according to a Harvard Business Review study, 64% of consumers say shared values are the primary reason they have a relationship with a brand. That was, by far, the most important motivator, with only 13% citing frequent interactions as the primary reason for a relationship. That is, while you must interact with your customers, the quality of your interactions is more important than the quantity in terms of brand perception.

 

To create a strong brand image, you must first understand who your brand is and what it stands for. This groundwork assists you in positioning yourself in the market and winning the hearts of your target customers. New and existing customers can both benefit from brand image-based social media programs, marketing, and large-scale pitching to individual consumers' interests.

 

How does Text mining impact Brand Image Analysis? 

 

Image analysis (also known as "computer vision" or "image recognition") refers to computers' ability to recognize attributes within images.

 

On your smartphone, do you use Google Photos or Apple's Photos app? They both use basic image analysis features to recognize and categorize faces in your photos, allowing you to view all of your photos of a specific person. Enter "dog" into the search function of either app to quickly find your puppy photos, or "beach" to find your tropical vacation photos.

 

Text analysis was the foundation of social media analytics and remains so today. Image analysis, on the other hand, is becoming increasingly important. Image analysis, when applied to social media analytics, is an extension of text analysis features applied to visual content.

 

Image analysis employs the same categorization methods. Rather than searching for all posts that contain the word "computer," object recognition can show you all posts that contain photos of a computer.

 

Every day, social networking sites like Facebook, Instagram, and Twitter generate massive amounts of textual data through posts, likes, and tweets. This data contains a wealth of information that we can use to generate new insights. Nonetheless, the majority of this data is unstructured and unsuitable for statistical analysis. Because approximately 80% of business data is unstructured, managing unstructured data for business benefit is understandable. With social media's exponential growth, its market share will continue to rise over time.

 

This massive amount of data can provide us with valuable insights, but it is highly unstructured and must be processed before it can be analyzed. By transforming a massive amount of unstructured customer feedback into actionable insights, analyzing this unstructured data in the text can assist marketers in customer experience management, brand monitoring, and other areas. 

 

When analyzing this massive amount of free-form text, one common issue is that no human can read it in a reasonable amount of time. Text mining is the solution in this case for dealing with unstructured data and unlocking the value of customer feedback. As an example, this article investigates how Tweets on a topic yielded valuable insights.

 

Also Read | Top 7 Text Mining Techniques

 

Text Mining Techniques: 

 

Text mining techniques can be understood by looking at the processes involved in mining text and gaining insights from it. These text-mining techniques are typically carried out using various text-mining tools and applications. Let us now examine the various text mining techniques: Some of the techniques used in Text Mining:

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Text Mining Techniques 

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1. Extraction of Information

 

This is the most well-known text-mining method. The process of extracting meaningful information from large amounts of textual data is referred to as information exchange. This text-mining technique focuses on extracting entities, attributes, and relationships from semi-structured or unstructured texts. 

 

The extracted information is then saved in a database for future access and retrieval. Precision and recall processes are used to check and evaluate the efficacy and relevance of the outcomes. Information Extraction is a technique that can be used to analyze textual data.

 

2. Categorization: 

 

Text classification is a text mining technique that is a supervised learning form in which ordinary language texts are assigned to a predefined set of topics based on their content. As a result, categorization, also known as NLP (Natural Language Processing), is the process of gathering text documents for processing and analysis to identify indexes or appropriate topics for the relevant document. 

 

The co-referencing method is commonly used in NLP. It extracts abbreviations and relevant synonyms from text data. Today's use cases for NLP analytics solutions range from spam filtering to personalized commercial delivery to web page categorization using tiered definitions.

 

3. Clustering:

 

One of the most important text-mining techniques is clustering. It attempts to identify intrinsic structures in textual data and organize them into relevant subgroups or "clusters" for further analysis. 

 

The formation of meaningful clusters from unlabeled textual data without prior knowledge is a significant challenge in the clustering process. Cluster analysis is a common text mining tool that aids in data distribution or serves as a pre-processing step for other text mining algorithms that operate on detected clusters. Clustering is the most well-known text-mining technique.

 

4. Summarization:

 

Text mining tools and techniques are now being used in a variety of industries, including academia, healthcare, law, finance, and many others. Text mining applications have seen a rapid rise as a result of this. Text mining applications that are currently in use include fraud management, business intelligence, social media analysis, customer service, and others.

 

This method allows you to obtain compressed versions of specific texts that contain useful information for the end user. Text summarization solutions enable you to search through various text sources to generate summaries of texts that contain large amounts of information but are insightful in a concise format. This technique preserves the meaning and intent of the original document.

 

5. Extraction of Data: 

 

Information extraction (IE) is a technique that uses Natural Language Processing to automatically extract a piece of definite, structured information from unstructured or semi-structured data in the form of text. It is used to extract entities from text, such as names of people, organizations, and locations, as well as the relationship between entities, attributes, events, and relationships.

 

The extracted data is well-organized (structured) and saved in a database for future use. Specific attributes and entities are extracted from the document and their relationships are established by IE. The process of checking and evaluating the relevance of results is known as 'Precision and Recall.'

 

6. Text Visualization: 

 

Text Visualization is a technique that converts large amounts of textual information into a visual map layout, allowing for enhanced browsing and simple searching. Visualization methods in text mining can help to improve and simplify the discovery of relevant information.

 

Text flags are used to represent individual documents or groups of documents, while colors are used to show density. Visual text mining organizes large amounts of text into a logical visual hierarchy, allowing the user to interact with the document by scaling and zooming.

 

Also Read | Top 10 Data Visualization Techniques
 

 

In conclusion, developing a strong brand image and identity does not happen overnight. It is measured through strategic interactions, understanding your customers, and understanding your competition.