Impact Factor (2025): 6.9
DOI Prefix: 10.47001/IRJIET
This study
investigates the possibilities of using social media platforms to raise output
and productivity in the apparel industry. Consumer behavior has a significant
impact on the fashion sector, thus businesses must incorporate social media
into their operations. The study looks at effective social media strategies and
campaigns used by clothing companies to evaluate their effects on output and
productivity. The proposal offers suggestions on how businesses use social
media platforms to create brands, interact with customers, and boost sales.
Large amounts of unstructured data, including user reviews, comments, and
hashtags, will be collected, and analyzed via social media sites using Natural
Language Processing (NLP) techniques. Topic modeling and sentiment analysis,
two techniques made possible by NLP, will assist discover significant themes
and customer impressions. Garment firms may improve their social media tactics
and ultimately increase efficiency and output by evaluating trends and patterns
in the data.
Country : Sri Lanka
IRJIET, Volume 7, Issue 10, October 2023 pp. 154-161
|
[1] |
W. M. &. T. H. Lim, "Social media as a tool
for brand awareness and consumer purchase intention," 2016. |
|
[2] |
S. Y. K. M. S. &. C. N. Park, "How do social
media influence collaboration in the fashion industry? Journal of Fashion
Marketing and Management: An International Journal," 2016. |
|
[3] |
W. W. S. L. a. M. L. ZX Guo, "Applications of
Artificial Intelligence in the Apparel Industry," Textile Research
Journal, p. 23, 2011. |
|
[4] |
N. Ahmad, "The Impact of Social Media on Fashion
Industry," p. 8, 2015. |
|
[5] |
R. D. M. Soeken, "Natural Language Processing for
Electronic Design," 2019. |
|
[6] |
S. Baier, "Analyzing Customer Feedback for
Product Fit Prediction," 2019. |
|
[7] |
D. K. H. A. Papenmeier, "Dataset of Natural
Language Queries for E-Commerce," 2023. |
|
[8] |
Y. L. R. Article, "Mining and Application of
Tourism Online Review Text Based on," 2022. |
|
[9] |
A. K. A. S. R. Poteet, Natural Language, 2007. |
|
[10] |
H. W. D. Dong, Multi-Task Learning for Multiple
Language Translation, 2015. |
|
[11] |
A. Karpathy, "Deep Visual-Semantic
Alignments," 2015. |
|
[12] |
S. K. B. N. Patro, "Multimodal Differential
Network for Visual Question Generation," 2018. |
|
[13] |
D. Wang, X. Liu and J. Liu, "The effects of
social media on brand awareness and brand equity," 2019. |
|
[14] |
H. Kim and Y. Sung, "Social media analytics of
consumer sentiment for fashion brands," 2018. |
|
[15] |
X. Li, X. Zhao and F. & Xue, "Social usage in
supply chain management," 2020. |
|
[16] |
N. Nizamuddin, S. S. Hishan and N. H. & Moin,
"Social media in supply chain management," 2019. |
|
[17] |
Y. Hou and Y. & Huang, "Customer engagement
with brands in social media," 2017. |
|
[18] |
N. Sahoo and S. & Nanda, "Social media
marketing for customer engagement," 2021. |
|
[19] |
C. F. C. &. M. S. Anderson, "Customer
Satisfaction and Shareholder Value. Journal of Marketing Research,"
2014. |
|
[20] |
J. &. M. D. Chevalier, " The Effect of Word
of Mouth on Sales: Online Book Reviews. Journal of Marketing Research,"
2006. |
|
[21] |
V. A. L. D. B. V. R. W. T. &. T. S. Kumar,
"Undervalued or Overvalued Customers: Capturing Total Customer
Engagement Value. Journal of Service Research," 2016. |