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Sentiment Analysis in Digital Marketing: Evaluating Success Dimensions of Sentiment Analysis and its Role in Digital Marketing

Erikson, Sofia LU and Lam, Welman (2024) SYSK16 20241
Department of Informatics
Abstract
This thesis explores the efficacy of sentiment analysis within digital marketing, examining its role and success through qualitative methodologies, including semi-structured interviews with industry professionals. As digital marketing evolves, understanding consumer sentiment becomes crucial for tailoring strategies that resonate with target audiences. Our study delves into the application of Natural Language Processing (NLP) techniques to analyze large datasets, focusing on sentiment analysis' capability to discern and categorize consumer emotions from digital interactions. Results indicate that sentiment analysis significantly enhances decision-making processes, enabling businesses to adjust their strategies based on real-time consumer... (More)
This thesis explores the efficacy of sentiment analysis within digital marketing, examining its role and success through qualitative methodologies, including semi-structured interviews with industry professionals. As digital marketing evolves, understanding consumer sentiment becomes crucial for tailoring strategies that resonate with target audiences. Our study delves into the application of Natural Language Processing (NLP) techniques to analyze large datasets, focusing on sentiment analysis' capability to discern and categorize consumer emotions from digital interactions. Results indicate that sentiment analysis significantly enhances decision-making processes, enabling businesses to adjust their strategies based on real-time consumer feedback. Challenges such as data volume and the complexity of human language are discussed, highlighting the need for advanced analytical tools. The research contributes to the field by confirming the integral role of sentiment analysis in modern digital marketing, suggesting that its strategic use can provide businesses with a competitive edge in understanding and responding to consumer behavior effectively. (Less)
Please use this url to cite or link to this publication:
author
Erikson, Sofia LU and Lam, Welman
supervisor
organization
course
SYSK16 20241
year
type
M2 - Bachelor Degree
subject
keywords
Sentiment Analysis, NLP, Digital Marketing, Big Data, D&M Model
language
English
id
9162621
date added to LUP
2024-06-13 13:29:10
date last changed
2024-06-13 13:29:10
@misc{9162621,
  abstract     = {{This thesis explores the efficacy of sentiment analysis within digital marketing, examining its role and success through qualitative methodologies, including semi-structured interviews with industry professionals. As digital marketing evolves, understanding consumer sentiment becomes crucial for tailoring strategies that resonate with target audiences. Our study delves into the application of Natural Language Processing (NLP) techniques to analyze large datasets, focusing on sentiment analysis' capability to discern and categorize consumer emotions from digital interactions. Results indicate that sentiment analysis significantly enhances decision-making processes, enabling businesses to adjust their strategies based on real-time consumer feedback. Challenges such as data volume and the complexity of human language are discussed, highlighting the need for advanced analytical tools. The research contributes to the field by confirming the integral role of sentiment analysis in modern digital marketing, suggesting that its strategic use can provide businesses with a competitive edge in understanding and responding to consumer behavior effectively.}},
  author       = {{Erikson, Sofia and Lam, Welman}},
  language     = {{eng}},
  note         = {{Student Paper}},
  title        = {{Sentiment Analysis in Digital Marketing: Evaluating Success Dimensions of Sentiment Analysis and its Role in Digital Marketing}},
  year         = {{2024}},
}