Empowering Sentiment Analysis For Improved Fashion Choices

AUTHOR(S)

Kavi Priya S, Porkodi J, Kaviya Sri A N, Shweatha K

DOI: https://doi.org/10.46647/ijetms.2023.v07i05.037

ABSTRACT
The world of fashion is dynamic, ever-changing, and deeply influenced by individual preferences and collective sentiment. Making confident and informed fashion choices can be a daunting task, especially in an era marked by rapid trends and diverse styles. The fashion industry is ever-evolving, with trends and styles constantly changing. Making informed fashion choices that align with personal preferences and public sentiment can be challenging. "Empowering Sentiment Analysis for improved fashion choices" presents a novel approach to address this challenge. This abstract introduces a comprehensive framework that harnesses the power of sentiment analysis, a sophisticated natural language processing technique, to provide consumers and fashion enthusiasts with invaluable insights into the realm of fashion. Sentiment analysis, commonly used in understanding public opinions and emotional tones in textual data, is adapted here to decode the fashion landscape. By analyzing textual data from fashion reviews, social media posts, and comments, sentiment analysis can discern public sentiment and opinions about specific clothing items, styles, and trends. The primary objective of this initiative is to empower individuals to make better, more informed fashion choices. Through sentiment analysis, individuals can access an in-depth understanding of the prevailing sentiments and opinions surrounding specific clothing items, styles, and trends. This knowledge equips them with the tools to align their choices with current trends, explore niche styles, or even express their uniqueness confidently. By leveraging the power of machine learning models like Logistic Regression, Naïve Bayes, Support Vector Machines, Random Forest , Ada Boosting and Deep Learning algorithms the sentiment classification models are built. Furthermore, this technology fosters inclusivity and diversity in fashion decision-making by highlighting a wide range of sentiments and opinions. It acknowledges that fashion is a highly personal and subjective domain and helps individuals discover styles that resonate with their unique tastes and values.

Page No: 319 - 324

References:

• Federico Becattini, Lavinia De Divitiis,  P.Y. Mok "Fashion recommendation based on style and social events" Multimedia Tools and Applications (2023)
• L. Javier Cabeza-Ramírez , Sandra M. Sánchez-Cañizares," Exploring the effectiveness of fashion recommendations made by social media influencers in the centennial generation, Textile Research Journal, Volume 93, Issue 13-14,February 16, 2023.
• Maryam Mahdikhani,” Exploring commonly used terms from online reviews in the fashion field to predict review helpfulness”,International Journal of Information Management Data Insights, Volume 3, Issue 1, April 2023.
• Hung-Jen Chen, Hong-Han Shuai,” A Survey of Artificial Intelligence in Fashion”, IEEE Signal Processing Magazine ,Volume: 40, Issue: 3, May 2023.
• Harmanjit Singh, Somnath Chakrabarti," How do gratifications to read reviews and perceived reviewers’ credibility impact behavioural intentions in fashion e-commerce? A mediating-moderating perspective." Computers in Human Behavior, Volume 143, June 2023.
• Pierfrancesco Bellini, Luciano Alessandro Ipsaro Palesi, Paolo Nesi,” Multi Clustering Recommendation System for Fashion Retail”, Sentient Multimedia Systems and Universal Visual Languages,January 13 2022.
• Artur M. Pereira, J. Antão B. Moura, Evandro De B. Costa,” Customer models for artificial intelligence-based decision support in fashion online retail supply chains”, Decision Support Systems, Volume 158, July 2022
• A Di Vaio, R Hassan, G D'Amore, R Tiscini,” Responsible innovation and ethical corporate behavior in the Asian fashion industry: A systematic literature review and avenues ahead”,Asia Pacific Journal of Management ,Springer 2022.
• Eunsoo Baek, Shelley Haines, Omar H. Fares,” Defining digital fashion: Reshaping the field via a systematic review”,Computers in Human Behavior , Volume 137, December 2022.
• N Liu, J Lin, S Guo, Fashion platform operations in the sharing economy with digital technologies: recent development and real case studies, Information- Transparent Supply Chains, Annals of Operations Research ,Springer(2022).
• S Sysko-Romańczuk, P Zaborek, A Wróblewska,” Data modalities, consumer attributes and recommendation performance in the fashion industry”,Electronic Markets,Springer 2022.
• Weiqian Li, Bugao Xu,” Aspect-Based Fashion Recommendation With Attention Mechanism”,IEEE Access , Volume 8 , 2020.
• Wei Zhou, P.Y. Mok,” Fashion recommendations through cross-media information retrieval”,Journal of Visual Communication and Image Representation , Volume 61, May 2019.
• Cairong Yan, Yizhou Chen” Differentiated Fashion Recommendation Using Knowledge Graph and Data Augmentation”,Volume 7,23 July 2019.
• Ana Paula Faria , Joana Cunha,” Fashion communication in the digital age: findings from interviews with industry professionals and design recommendations”, Procedia CIRP, Volume 84, 2019.



How to Cite This Article:
Kavi Priya S, Porkodi J, Kaviya Sri A N, Shweatha K . Empowering Sentiment Analysis For Improved Fashion Choices . ijetms;7(5):319-324. DOI: 10.46647/ijetms.2023.v07i05.037