EMOTION DETECTION ON KENYAN TWEETS USING EMOTION ONTOLOGY
DOI:
https://doi.org/10.47672/ejt.385Keywords:
hash tag, emoji, GIF’s, adjective, emotion ontologyAbstract
Purpose: The purpose of the study was emotion detection on Kenyan tweets as a powerful tool in detecting and recognizing the various feelings among netizens and provide critical analytics that can be used in various platforms for decision making.
Methodology: This research study adopted a descriptive research design approach. The researcher preferred this method because it allowed an in-depth study of the subject. The target population will be twitter account holders with twitter followers ranging between 100,000 up to 2000, 000 in Kenya. Data was analyzed using descriptive and inferential statistics. The study will employ a census approach to collect data from the respondents hence no sampling techniques will be used. According to Larry (2013) a census is a count of all the elements in a population. The sample size will be the 150 respondents .Quantitative data was analyzed using multiple regression analysis. The qualitative data generated was analyzed by use of Statistical Package of Social Sciences (SPSS) version 20.
Results: The response rate of the study was 64%.The findings of the study indicated that hashtags, emojis, GIF’s and adjectives have a positive relationship with emotion detection in Kenya.
Conclusion: R square value of 0.715 means that 71.5% of the corresponding categorization in emotion ontology can be explained or predicted by (hashtags, emojis, GIF’s and adjectives) which indicated that the model fitted the study data. The results of regression analysis revealed that there was a significant positive relationship between dependent variable and independent variable at (β = 0.715), p=0.000 <0.05).
Policy recommendation: Finally, the study recommended that twitter account holders should embrace various emotion detecting platforms so as to improve how they articulate issues and further researches should to be carried out in other social media platforms to find out if the same results can be obtained.
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