Visualizing Trends and Correlation Between Fashion Features for Product Design Prediction Using Classification and Sentiment Analysis ^†

Abstract

To demonstrate the interrelation of fashion elements for design forecasting, the research examines classification and sentiment analysis methodologies. The study combines survey data with information from social media and e-commerce sites to find important emotional and behavioral patterns that affect how people make buying decisions. The research employs deep learning, Logistic Regression, and Random Forest models to predict design trends and user preferences. The research methodology focuses on improving fashion analytics through feature selection and user segmentation and visual storytelling methods to enhance strategic decision-making.

1. Introduction

The process of visualizing trends between features in classification and sentiment analysis helps users discover concealed data patterns, which leads to better decision-making. Analysts who unite machine learning methods with visual presentation techniques can study how various features, including word frequency, user actions, and time-dependent variables, affect both classification results and sentiment measurements [1]. The analysis of sentiment patterns through time reveals how public opinions change, while heatmaps of feature correlations show which variables drive results and which ones duplicate information. The visualizations improve model understanding as they present complicated findings in an easier-to-understand format. The fashion industry uses data mining to analyze market trends and customer actions, which helps brands create better products and forecast consumer behavior. The fashion industry now has unlimited digital content available through social media platforms, influencer fashion, online product reviews, and e-commerce transaction records, which can be analyzed through data mining [2]. The process of data mining uses machine learning, statistical analysis, and natural language processing to extract valuable information from extensive datasets. In fashion, it helps brands achieve the following:

• Forecast trends: The system uses runway images, influencer content, and retail sales data to generate future market predictions.
• Understand consumer preferences: The system uses review data, purchase records, and user browsing patterns to determine what customers want
• Personalize recommendations: The system generates individualized product suggestions through user segmentation based on their fashion choices, measurement details, and shopping patterns
• Optimize inventory and pricing: The system uses predictive models to determine optimal inventory levels and prices through analysis of market demand and seasonal patterns.

Studying how people use digital platforms, goods, and services in depth and methodically is known as understanding user behavior. It begins with the collection of data using various tools and techniques, including event-based analytics, click tracking, heatmaps, and session recordings. These enable us to observe user behavior, including where users click, how long they stay on a page, what they ignore, and where they end up [3].

For efficient analysis and optimization, it is critical to keep an eye on a few key feature types when tracking user interactions. Examples of demographic data that supports user segmentation and experience personalization based on audience characteristics include age, gender, and location. Behavioral data indicates possible usability problems and shows how involved users are. Device data, like a user’s desktop or mobile device, is essential for design optimization and cross-platform compatibility. Session data helps pinpoint interaction process pain points and offers insights into the user journey. Lastly, we can monitor customer loyalty tactics with the help of purchase history. Classification is a supervised learning technique where an algorithm learns from a labeled dataset to predict the class of new, unseen data. Each data point is associated with a category (or class), and the model tries to learn the relationship between the features and the class labels. Opinion mining, another name for sentiment analysis, is a potent method in data mining that focuses on extracting and interpreting the emotional tone behind textual data. NLP algorithms are the most effective and easy tools for sentiment analysis. Reviewing papers on sentiment analysis, it was found out that the algorithms that are majorly used for sentiment analysis are NLP algorithms [4].

2. Literature Review

Table 1. Summary of use of feature selection and classification in fashion data mining.

S. No	Paper Title	Authors	Data Mining Technique Used	Dataset	Limitations
1	Data Efficient Training with Imbalanced Label Sample Distribution for Fashion Detection	Xin Shen, Praful Agrawal, Zhongwei Cheng [1]	Deep neural network (DNN)-weighted objective function for multi-label classification with long-tailed data distribution	Fashion attribute classification dataset (specific dataset details not explicitly mentioned)	Identified substantial errors in experimental results and a potentially misleading explanation of the algorithm
2	PAI-BPR: Personalized Outfit Recommendation Scheme with Attribute-wise Interpretability	Dikshant Sagar, Jatin Garg, Prarthana Kansal, Sejal Bhalla, Rajiv Ratn Shah, Yi Yu [2]	Attribute-wise interpretable compatibility scheme with personal preference modeling	IQON3000 publicly available real-world dataset	Subjectivity in fashion compatibility modeling; need for personalization in recommendations
3	Tailor: Size Recommendations for High-End Fashion Marketplaces	Alexandre Candeias, Ivo Silva, Vitor Sousa, José Marcelino [3]	Combines explicit (ReturnReason) and implicit (Add2Bag) user signals in sequence classification method	Historical transactional data from high-end fashion marketplaces	Evaluated usability in real-time recommendation scenarios, measuring latency performance
4	Computational Technologies for Fashion Recommendation: A Survey	Yujuan Ding, Zhihui Lai, P. Y. Mok, Tat-Seng Chua [5]	Various computational techniques for fashion recommendation, including personalized product recommendation, complementary (mix-and-match) recommendation, and outfit recommendation	Multiple datasets used in fashion recommendation studies (specific datasets not explicitly listed)	Identifies gaps between academic research and real-world fashion industry needs
5	Knowledge Enhanced Neural Fashion Trend Forecasting	Yunshan Ma, Yujuan Ding, Xun Yang, Lizi Liao, Wai Keung Wong, Tat-Seng Chua [6]	Knowledge-Enhanced Recurrent Network (KERN) leveraging internal and external domain knowledge	FIT dataset collected from Instagram	Challenges in capturing complex patterns of fashion elements; need for further refinement in trend prediction

below summarizes review of classification techniques used in fashion data mining and sentiment analysis. In reference [5], a comprehensive survey reviews recent advancements in computational technologies for fashion recommendation. It categorizes different fashion recommendation tasks and discusses problem formulations, research focuses, state-of-the-art methods, and limitations.

In reference [1], the challenge of imbalanced data distribution in multi-label classification models for fashion detection is addressed. Experiments on image-based attribute classification of fashion apparel demonstrate the effectiveness of the proposed method. While the paper discusses multi-label classification challenges in fashion detection, it does not explicitly list the datasets it uses. However, common datasets used in fashion detection research include the following:

• DeepFashion2: A large-scale dataset with diverse fashion images and detailed annotations.
• Kaggle Fashion Datasets: Various datasets available for fashion-related machine learning tasks.

Reference [6] introduces a knowledge-enhanced neural network model for forecasting fashion trends. It incorporates domain knowledge into the classification process, using a large-scale fashion trend dataset collected from Instagram.

According to [3], in the fast-paced and constantly changing environment of high-end fashion markets, delivering precise and individualized size guidance has emerged as a make-or-break factor. Satisfying the expectations of customers in this area is not only essential to guarantee their happiness but also takes center stage in fueling customer loyalty, an important indicator of the success of any fashion retailer

Reference [2] offers a customized outfit recommendation system with attribute-level interpretability. It utilizes classification techniques to model user preferences, using the IQON3000 dataset.

Sentiment Analysis Using NLP Algorithms in Fashion Data Mining

Table 2, below, summarizes the study of fashion-focused sentiment analysis using NLP. Reference [7] uses Twitter and T4SA datasets to forecast fashion trends with NLP and time-series modeling. This paper demonstrates how emotional signals from platforms like Twitter can be mined to forecast fashion trends. combines NLTK and GPT with ML models to analyze customer reviews in fashion retail. This paper explores multiple ML algorithms—SVM, Logistic Regression, and neural networks—to analyze user reviews on e-commerce platforms. It finds that hybrid models and word embeddings significantly improve sentiment classification accuracy.

Reference [8] applies Naive Bayes and TF-IDF to women’s apparel reviews, focusing on polarity and subjectivity. Using traditional NLP techniques like TF-IDF and Naive Bayes, this study classifies customer reviews into positive, negative, and neutral sentiments.

Reference [9] employs pre-trained word embeddings, LSTM, and CNN models, and targets review classification for fashion items. The study uses CNNs and Word2Vec embeddings to capture complex emotional patterns in reviews of fashion products. When it comes to handling complex linguistic structures, deep learning models perform better than traditional methods.

Fashion & Consumer Behavior: Influencer marketing is shown to be a powerful tool in shaping consumer trust and loyalty. The study highlights how digital platforms amplify brand visibility and create authentic engagement, ultimately driving purchase decisions [10]. Sentiment analysis of women’s clothing reviews reveals nuanced emotional responses, from satisfaction with fit to frustration with quality. These insights help fashion retailers tailor products and improve customer experience [9]. AI applications in financial risk management improve predictive accuracy, enabling companies to mitigate risks and strengthen decision-making processes [11]. K-means clustering is applied to fashion consumer data, uncovering distinct preference groups. This segmentation supports personalized marketing strategies and enhances predictive modeling of future trends [12]. Raw clothing datasets provide a foundation for computational analysis, supporting research in consumer behavior, fashion trends, and predictive modeling [13]. The SAND framework enhances LLM agents by enabling self-taught deliberation, improving their ability to plan, reason, and act autonomously in complex tasks [14]. Fashion consumption in emerging markets is influenced by cultural identity, affordability, and aspirational choices. The study emphasizes the importance of regional context in shaping consumer demand [15]. Data mining techniques such as clustering, classification, and association rules are reviewed as effective tools for analyzing consumer behavior. The paper emphasizes their role in extracting actionable insights from large datasets [16]. Feature selection methods are compared, showing how reducing irrelevant variables improves efficiency and accuracy in predictive models. This contributes to more reliable consumer analytics [17]. Mining electronic customer behavior uncovers hidden purchase patterns, helping businesses design targeted marketing campaigns and loyalty programs [18]. The study proposes an augmented method to select fashion talent by integrating social media characteristics.

It highlights how online presence and engagement can complement traditional evaluation criteria [19]. Social media characteristics are integrated into fashion talent selection, showing how online presence and engagement metrics can complement traditional evaluation methods [20]. Statistical analysis of student shopping behavior highlights affordability, convenience, and peer influence as key drivers of online purchases [21]. Apparel sizing systems benefit from data mining, which enables mass customization by aligning garment dimensions with consumer body data. This reduces returns and enhances satisfaction [22]. Industrial clusters in textiles foster innovation, collaboration, and competitiveness, strengthening the overall industry ecosystem [23]. Association rules and clustering applied to online shopping data reveal frequent purchase combinations and customer segments, supporting strategic product bundling [24]. Data mining of online shopping behavior reveals predictive patterns, enabling e-commerce platforms to anticipate consumer needs and optimize recommendations [25]. Data mining applied to portfolio optimization demonstrates how computational techniques can balance risk and return, offering investors more robust strategies [26]. Association rules are demonstrated as decision-making models in textile operations, guiding production planning and resource allocation [27]. Garment categorization using data mining techniques improves classification accuracy, supporting fashion analytics and inventory management [28].

Table 2. Summary of fashion-focused sentiment analysis.

S. No	Paper Title	Authors	Data Mining Technique Used	Dataset	Limitations
1	Sentiment Analysis Using NLP and ML in Fashion E-commerce	J. I. Nandalwar et al. [4]	NLTK-Models	Fashion Reviews	Generalized ML pipeline; limited explainability and interpretability
2	Sentiment Analysis of Fashion Product Reviews Using Deep Learning	S. K. Singh Verma [9]	Naïve Bayes	Fashion Reviews	Requires computational; less effective datasets
3	Emotional Analysis of Fashion Trends Using Social media and AI	Aayam Bansal, Agneya Tharun [7]	NLP, Time-Series (ARIMA, Granger Causality)	Twitter, T4SA	Limited to short-form text; lacks product-specific granularity
4	Sentiment Analysis of Women’s Clothing Reviews	Kunal Choudhary, Dr. Sapna Sinha [8]	Naïve Bayes	Reviews	Focuses only on polarity/subjectivity; lacks aspect-level sentiment

Table 2. Summary of fashion-focused sentiment analysis.

S. No	Paper Title	Authors	Data Mining Technique Used	Dataset	Limitations
1	Sentiment Analysis Using NLP and ML in Fashion E-commerce	J. I. Nandalwar et al. [4]	NLTK-Models	Fashion Reviews	Generalized ML pipeline; limited explainability and interpretability
2	Sentiment Analysis of Fashion Product Reviews Using Deep Learning	S. K. Singh Verma [9]	Naïve Bayes	Fashion Reviews	Requires computational; less effective datasets
3	Emotional Analysis of Fashion Trends Using Social media and AI	Aayam Bansal, Agneya Tharun [7]	NLP, Time-Series (ARIMA, Granger Causality)	Twitter, T4SA	Limited to short-form text; lacks product-specific granularity
4	Sentiment Analysis of Women’s Clothing Reviews	Kunal Choudhary, Dr. Sapna Sinha [8]	Naïve Bayes	Reviews	Focuses only on polarity/subjectivity; lacks aspect-level sentiment

3. Visualizing Trends Using Classification and Sentiment Analysis

Data collected can be analyzed to visualize trends using classification and sentiment analysis. Steps are given in Figure 1 below.

Step 1: Data Collection

A.

Demographic Data

To learn more about your customers, gather basic information about them.

• Age: To examine preferences across age groups, it is necessary to group them (e.g., 18–25, 26–35, 36–50).
• Gender: This factor aids in determining the appeal of a product; for example, men may favor darker hues, while women may favor pastel tones.
• Location: Regional seasonal trends differ, as do urban versus rural preferences
• Income Level: This shows purchasing power; consumers with higher incomes might favor luxury brands.

B.

Transaction Data

Keep tabs on what and when customers are purchasing.

Product categories, e.g., dresses, shoes, accessories.

• Quantity Sold: This indicates how popular a product is.
• Sales Amount: This helps to find products with worth and money.
• Time of Purchase: Seasonal trends affect when people buy things (for example, sweater sales peak in November).

C.

Sentiment Data

Be mindful of how your customers feel and what they think.

• Product Reviews and Ratings: Written comments like “Too tight” or “Loved the fit”.
• Answers to surveys may include simple thoughts on comfort, style, cost, etc.

To look into user behavior and emerging trends, we employ a hybrid strategy that blends primary data collection via surveys with secondary data mining from online sources. A thorough grasp of user preferences, feelings, and interaction patterns is ensured by this dual approach.

Survey-Based Data Collection (Primary Source)

• To obtain user thoughts, preferences, and feelings about particular goods, fashions, or experiences, a structured questionnaire is created.
• To gather qualitative information, the survey has both open-ended and closed-ended questions (like multiple-choice and Likert scale ratings).
• Sentiment analysis techniques are used to preprocess and analyze responses in order to classify user intent and extract emotional tone.
• This helps the target audience directly identify subjective trends and user expectations.

Online Dataset Mining (Secondary Source)

• Openly accessible datasets are gathered from sites like social media, fashion blogs, review portals, and e-commerce websites.
• User-generated content such as product reviews, ratings, comments, and behavioral logs (such as clicks, purchases, and browsing history) are included in these datasets.
• Black Friday sales, Brazilian e-commerce, Big Mart sales, and sales datasets are among the ones we use.
• Mendeley Raw Clothing Data: This dataset includes Amazon reviews of clothing products. These data can be utilized for data analytics and academic purposes.
• Hugging Face Datasets/Amazon Reviews/2023/McAuley Lab: This large collection of Amazon reviews was collected by McAuley Lab in 2023 and includes rich data such as user reviews, item metadata, and links.

To validate results and enhance the analysis, insights from both sources are combined and examined. To validate results and improve the analysis, insights from both sources are combined comparative research is carried out to identify trends, sentiment distribution, and classification. Results are displayed using visualization tools (such as Seaborn and Tableau).

Step 2: Data Preprocessing

• Cleaning

Eliminate duplicates, missing entries (like “age not filled”), and mistakes (like “M” instead of “Male”).

• Sentiment Analysis

To extract emotions from text, use natural language processing (NLP) tools.

• Tokenization: Dividing text into individual words.
• Sentiment scoring: Using programs like VADER or BERT, assign scores (for example, +0.8 for “excellent quality,” −0.6 for “poor stitching”).

Step 3: Data Analysis

Make patterns visible and understand them.

• Trend Visualization—Line graphs illustrating festival-related sales spikes.
• Correlation Analysis—For instance, repeat business is frequently associated with high ratings.
• Summary Metrics: Average sales by age group and sentiment scores by product type.

Step 4: Model Selection for Sales Forecasting

• To forecast future sales, select models:
• Linear Regression—This approach forecasts sales by utilizing sentiment and price.
• Random Forest—These models manage numerous variables and intricate relationships.

Step 5: Product Design Prediction

• Feature Selection

Decide which facts are most crucial.

Age, sentiment score, and product category, for example, may all have a large influence on design choices.

• Classification Models

Find out which designs will be popular.

○

Logistic Regression—This method generates binary results, such as will buy vs. will not buy.

○

Decision Trees—These offer a visual representation of the decision-making process.

○

Random Forest Classifier—For increased accuracy, the RFC combines several trees.

○

Model training and evaluation.

Check the model performance.

○

Accuracy—This reflects total correctness.

○

Precision—This reflects the number of accurate positive forecasts.

○

Recall—This is the model’s capacity to locate all pertinent cases.

Step 6: Conclusion

Practical insights into fashion product design may be achieved through the following considerations.

○

Feature Importance: For example, sentiment score may be more significant than price.

○

Customer segmentation: Assign users to groups according to their inclinations (e.g., budget-conscious vs. trend-focused)

○

Marketing and Design Strategy: Use research to create eye-catching collections and campaigns.

4. Conclusions

The study shows that a strong framework for comprehending customer preferences and forecasting product design trends in the fashion industry can be obtained by fusing sentiment analysis with classification models. The study finds significant relationships between demographic characteristics, emotional feedback, and buying patterns by examining survey responses as well as online behavioral data. Sentiment signals and transaction history are important factors that influence design appeal, according to feature importance analysis. Through data-informed strategies, the findings support the use of AI-driven tools to optimize inventory, personalize fashion offerings, and improve user experience.