The Effects of Stress and Chatbot Services Usage on Customer Intention for Purchase on E-Commerce Sites
Ethics reference is: 2020/2144326527-03
The aim of this research is to power chatbots with algorithms that can determine a potential buyer from customers’ chats to offer them a sale. To reach our goal, detecting the potential customer from the chat is the main challenge that we have to overcome. Discovering emotions from chat will direct us to understand more about customers’ intention to purchase or accept an offer.
Experimental (empirical) research is defined as data-based research which relays on experiments or observations. Moreover, in experimental research, a verifiable conclusion should be generated by the researcher. Therefore, we developed a hypothesis and established an experimental design to prove or disprove it.
The Null Hypothesis (H0): There is no relation between user emotion to their online buying decision-making.
The Alternative Hypothesis (H1): User emotions play a significant role in online purchasing decision-making.
To prove or disprove this hypothesis, experimental research with a positive approach has been designed. The goal of this experimental research is to find out whether there is a relation between users’ emotions and their purchasing decision-making process. We found four datasets that are labelled with emotion tags and then filtered them based on the conversation about purchasing (both accepting and declining purchases).
4 datasets as well as references which have been used to test hypothesis on this research:
EmotionLines: Dialogues extracted from the Friends TV Series are labelled by Basic emotion: Anger, Disgust, Fear, Happiness, Sadness, and Surprise. The dialogue emotions were identified by humans in a survey.
LREC 2018 - 11th International Conference on Language Resources and Evaluation
Chen, S. Y., Hsu, C. C., Kuo, C. C., Huang, T. H. K., & Ku, L. W. (2019). Emotionlines: An emotion corpus of multi-party conversations.
CARER:Tweets extracted from the tweeter. They are in English Language and their emotions were identified by their authors' given hashtags. Emotions are Anger Anticipation, Disgust, Fear, Joy, Sadness, Surprise, and Trust.
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, EMNLP 2018
Saravia, E., Toby Liu, H. C., Huang, Y. H., Wu, J., & Chen, Y. S. (2018). Carer: Contextualized affect representations for emotion recognition.
, 3687–3697. https://doi.org/10.18653/v1/d18-1404
EmotionPush:Messages are extracted from Facebook Messenger with 7 emotions: Joy, Anticipation, neutral, tired, anger, fear, and sadness
2018 IEEE Global Communications Conference, GLOBECOM 2018 - Proceedings
Huang, C. Y., & Ku, L. W. (2018). EmotionPush: Emotion and Response Time Prediction Towards Human-Like Chatbots.
GoEmotions:The datasets are extracted from Reddit comments based on 27 emotions.
GoEmotions: A Dataset of Fine-Grained Emotions
Demszky, D., Movshovitz-Attias, D., Ko, J., Cowen, A., Nemade, G., & Ravi, S. (2020).
. 4040–4054. https://doi.org/10.18653/v1/2020.acl-main.372
Is this dataset for graduation purposes?