Sentiment Analysis in Digital Marketing: Evaluating Success Dimensions of Sentiment Analysis and its Role in Digital Marketing
(2024) SYSK16 20241Department 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:
http://lup.lub.lu.se/student-papers/record/9162621
- author
- Erikson, Sofia LU and Lam, Welman
- supervisor
- organization
- course
- SYSK16 20241
- year
- 2024
- 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}}, }