How to Do Text-Based Personality Prediction Using ML

Online businesses can attract customers from around the world—and while no two customers are alike, certain groups share similarities and preferences. As a result, understanding your customers' personalities is crucial, and doing so will allow you to determine which customers will respond positively to targeted marketing campaigns and personalized product offerings.

The best way to learn about your customers is by analyzing their textual correspondence—including reviews, emails, and social media interactions. Unfortunately, analyzing this data using traditional methods can be a time-consuming task that's prone to inaccuracies. However, machine learning (ML) offers an efficient alternative capable of analyzing large amounts of text to identify patterns indicative of certain personality traits.

We'll explore the benefits of using ML in this blog post, and how Akkio, a predictive AI platform, allows users to create ML models that aid with personality detection. By analyzing text-based data, Akkio can ultimately help you gain valuable insights into your customers' preferences.

Valid Use Cases for Personality Detection

Personality detection using ML has several practical applications that can help businesses boost their marketing, customer engagement, and product development. Let's take a closer look at a few of these applications:

1. Customer Segmentation: By clustering customers into different segments based on their personality traits, businesses can deploy tailored strategies for each group. For example, one segment might be comprised of curious users that require constant updates and content to stay engaged, while another segment may contain rational users that just need reminders about the money they're saving.
2. Improve Your Marketing Campaigns: Personality detection can help businesses better understand customers' preferences, needs, and behaviors. Analyzing textual data from sources such as customers' social media posts, emails, or chat logs with the company can help businesses gain insights into what customers care about, what motivates them, and what they're more likely to buy.
3. Personalize Your Products: Businesses can use personality data to create user personas, each with different needs and preferences, and develop products tailored to specific personality types.
4. Improve Customer Engagement and Retention: By tailoring their messages and offerings to the personality traits of their customers, businesses can create a more personalized and compelling customer experience. In turn, this can increase loyalty and satisfaction, and improve customer retention.

Recruitment and Hiring: A Dangerous Use Case for Personality Detection

You might wonder if it's possible to apply personality detection to recruitment and hiring efforts—and it is, but it's risky. Using artificial intelligence (AI) for this purpose can lead to biases and have severe repercussions in the long run.

For example, one AI algorithm used as a recruitment tool was found to be sexist, contributing to unfair hiring practices. With this in mind, we don't recommend using personality detection through AI for recruitment and hiring. Instead, it's much safer (and more effective) when used in marketing campaigns, customer engagement, and product development.

Ethical considerations shouldn't be overlooked when it comes to ML and text-based personality prediction, either. Compliance with data protection laws (like GDPR) prevents misuse, bias, and invasion of privacy, and ensures customers that your business respects their user rights.

How Machine Learning Helps With Text-Based Personality Detection

ML can analyze data more efficiently and accurately, and even help businesses understand their customers and offer personalized experiences via text-based personality detection. So, let's dive into some of the key concepts that make up ML-enhanced personality detection:

Efficient Data Analysis

Language is complex and diverse, and prediction models need to understand various linguistic structures, contexts, and nuances. The more data the algorithm processes, the better it becomes at identifying patterns and making accurate predictions.

As a result, in text-based personality detection, the ability to process massive amounts of data quickly and efficiently is invaluable. Over time, as the algorithm encounters more and more data, it refines its understanding of the patterns, leading to more accurate predictions. ML can save time and resources, especially compared to traditional analysis methods, and automate the process of identifying patterns and correlations in the data to help businesses make more informed decisions about their customers' personalities.

Deep Learning

Deep learning is a subset of ML that uses convolutional neural networks to identify patterns in large datasets—and language models are AI algorithms trained to understand, generate, or manipulate human language. Deep learning-based language models can be used to analyze large datasets of text-based communication and identify subtle linguistic features indicative of personality traits.

For example, deep learning algorithms can effectively predict a person’s personality based on their writing style, choice of words, and other linguistic features. As a result, deep learning-based language models can be used to tailor marketing campaigns and product recommendations to individual customers, making them more relevant and engaging, and even power chatbots and virtual assistants, ensuring they provide prompt, accurate, and personalized support.

Natural Language Processing (NLP)

NLP enables computers to understand the meaning and context of human language, making them an essential component of text-based personality detection. Additionally, NLP techniques can extract relevant information from textual data from interactions with customers, such as social media, emails, or chat logs. By training on extensive datasets, these ML algorithms develop a comprehensive understanding of language patterns, enabling them to adapt to new, unseen text inputs.

One NLP technique used in text-based personality detection is sentiment analysis. Sentiment analysis discerns the emotional tone behind a person's words by evaluating positive, negative, or neutral sentiments in text data, and can help identify patterns in a person's communication style. Another NLP technique, named entity recognition (NER), identifies and classifies key entities within the text, such as names, organizations, or locations. In the context of personality detection, NER aids in understanding a person's social environment, interests, and affiliations, enriching the extracted personality traits.

Text Classifiers

A type of supervised learning, text classification involves training an ML algorithm to classify text data into different categories. These text classification algorithms can identify personality-related behaviors that help businesses interact efficiently with their customers.

Moreover, text classification can handle noisy and unstructured data and be easily integrated with other ML models, making it a powerful tool for text-based personality detection. For instance, after applying NLP techniques like sentiment analysis and named entity recognition to preprocess and extract relevant features from the text, these features can be used as input for text classification models. The models then predict the appropriate personality traits or types based on the features.

Data Sources for Personality Detection Analysis

A massive benefit of using ML for personality prediction is its ability to handle large amounts of data from various sources. These data sources can then be combined into a unified view using tools and techniques such as data warehouses, data lakes, and APIs.

You can take your pick of data sources to enhance your ML-assisted personality detection. Still, the best data to use is textual data that comes directly from customer interactions with the business. These sources give your business the most accurate representation of your customers – and they're ethically and legally sourced!

Common data sources include:

1. Customer Service Interactions: Interactions like emails, chat logs, and phone conversations provide a valuable source of text-based communication data.
2. Online Reviews: Product reviews can be taken from Amazon, Yelp, or TripAdvisor, and contain a wealth of text-based data.
3. Social Media: Social networks like Twitter, Facebook, Instagram, and LinkedIn are also rich sources of text-based communication data. Remember to analyze samples of customers interacting with your business on social media for the best results.
4. Video and Podcast Transcriptions: Content from videos or podcasts, when transcribed, can serve as text data for analyzing the personalities of speakers based on their language use, tone, and topics discussed.
5. Research Studies: Academic research often includes high-quality communication data, like interview transcripts, survey responses, or social media posts.
6. Self-report Surveys: These surveys are a popular, but problematic, way to collect data. Self-report surveys can introduce inherent bias, as they rely on what people say about themselves rather than how they actually behave. While they can provide insights, they may not be as accurate as other data sources due to the potential for self-reporting biases. Common self-report surveys include the Big Five:

1. Big Five Inventory (BFI): BFI, also known as OCEAN or CANOE, is a widely-used self-report survey that assesses the big five personality models of personality: openness, conscientiousness, extraversion, agreeableness, and neuroticism.
2. NEO Personality Inventory: A survey commonly used to diagnose personality disorders in clinical settings. NEO is also used in research settings to investigate the relationship between personality and other metrics, like mental health, academic performance, and job performance.
3. Myers-Briggs Type Indicator (MBTI): The Myers-Briggs Type Indicator is a popular personality test that assesses individuals' preferences on four dichotomous dimensions: extraversion-introversion, sensing-intuition, thinking-feeling, and judging-perceiving.
4. State-Trait Anxiety Inventory (STAI): The State-Trait Anxiety Inventory is a questionnaire used to measure anxiety as a temporary state (state anxiety) and a more enduring characteristic (trait anxiety).

Using Akkio for Personality Detection

Akkio is a versatile predictive AI platform suitable for a wide variety of business applications, including personality detection. And because Akkio is easy to implement, you don't need extensive knowledge of ML to harness its power!

What's more, Akkio can automate many processes for you, including pre-built text classification – all you need to do is connect your data sources. Akkio will also choose the optimum model type based on your needs. This is usually a language model for text-based personality detection, and examples of these models include Decision Trees, Logistic regression, Random Forest, Support Vector Machines (SVM), and Naive Bayes.

But don't worry if you're unfamiliar with these models. As an Akkio user, you don't have to worry about the intricacies!

Akkio is also compatible with any of the data sources mentioned in this post, like self-report surveys, social media posts, and reviews. The platform can also handle a variety of data formats and sources, including tables in CSV, Excel, JSON, and PARQUET formats. Additionally, Akkio integrates directly with Google Sheets, Hubspot, Snowflake, Google Big Query, Salesforce, and Zapier.

Your data will be prepared for analysis once you've connected it to Akkio. Then, after setting your parameters, the platform will identify patterns related to your customers' personalities based on the text data provided.

Learn More About Your Customers’ Personalities with Akkio

Getting to know your customers with personality detection is essential—especially if you want to take your marketing campaigns to the next level. Personality detection can also help businesses develop personalized product offerings and enhance customer engagement and retention.

Akkio's platform lets you harness the power of ML-assisted personality detection. The no-code platform enables users without a computer science or programming background to build, deploy, and manage AI models easily. And, thanks to Akkio's quick model training, you can streamline the development process by creating and testing models within minutes.

Akkio is an AI and ML platform that makes AI accessible to non-technical users. The no-code platform enables users without a computer science or programming background to build, deploy, and manage AI models easily. And, thanks to Akkio's quick model training, you can streamline the development process by creating and testing models within minutes.

Akkio has everything you need to start reaping the benefits of text-based personality detection for yourself. So, don't miss out on your chance to gain customer insights, make more informed decisions, and benefit your business: sign up for Akkio or start your free trial today!