Detecting consumer emotions on social networking websites
Madhala, Prashanth (2019)
2019
Industrial Engineering and Management
Tekniikan ja luonnontieteiden tiedekunta - Faculty of Engineering and Natural Sciences
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Hyväksymispäivämäärä
2019-05-24
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:tty-201905311829
https://urn.fi/URN:NBN:fi:tty-201905311829
Tiivistelmä
The social networking environment goes beyond connecting friends. It also connects customers with companies and vice versa. Customers share their experience with friends, followers, and companies and these experiences carry sentiments and emotions thereby creating big data. There is an ocean of data that is available for companies to extract and make meaning out of it by applying to different business contexts such as consumer feedback analysis and marketing & communications. For companies to benefit from consumer emotion data, they must make use of computational methods that can save time and work consumed by traditional consumer research methods such as questionnaires and interviews.
The objective of this research is to explore existing literatures on detecting consumer emotions from social networking data. The author carried out a systematic literature review on research articles from three bibliographic databases with the intent to find out social networking data extraction process, dataset sizes, computational methods used, consumer sentiments, emotions studied, limitations and its application in a managerial context. To further understand consumer emotion detection, a case study in the form of a Twitter marketing campaign was conducted to emulate the process of consumer emotion detection on a company that is selling stress management products and services.
The results indicate that most companies use Twitter networking platform to carry out consumer emotion analysis. The dataset sizes range from small to very large. The studies have used variety of computational methods, some with accuracies to measure the performance. These methods have been applied in various industries such as travel, restaurant, healthcare, and finance to name a few. Managerial applications include marketing, supply chain, feedback analysis, product development, and customer satisfaction. There are few limitations that were identified from using these methods. The case study results and discussion with the case company CIO communicated the potential for the use of some of the methods for consumer behavior research. The valuable feedback from the CIO revealed that by customizing existing methods, their company can create new tools and methods to understand their customers by providing better recommendations and customize their offerings to individual customers.
The objective of this research is to explore existing literatures on detecting consumer emotions from social networking data. The author carried out a systematic literature review on research articles from three bibliographic databases with the intent to find out social networking data extraction process, dataset sizes, computational methods used, consumer sentiments, emotions studied, limitations and its application in a managerial context. To further understand consumer emotion detection, a case study in the form of a Twitter marketing campaign was conducted to emulate the process of consumer emotion detection on a company that is selling stress management products and services.
The results indicate that most companies use Twitter networking platform to carry out consumer emotion analysis. The dataset sizes range from small to very large. The studies have used variety of computational methods, some with accuracies to measure the performance. These methods have been applied in various industries such as travel, restaurant, healthcare, and finance to name a few. Managerial applications include marketing, supply chain, feedback analysis, product development, and customer satisfaction. There are few limitations that were identified from using these methods. The case study results and discussion with the case company CIO communicated the potential for the use of some of the methods for consumer behavior research. The valuable feedback from the CIO revealed that by customizing existing methods, their company can create new tools and methods to understand their customers by providing better recommendations and customize their offerings to individual customers.