I hold a Bachelor's degree in Business Administration and a Master's degree in Digital Innovation. For over three years, I worked for a multinational IT company in Ireland on a Natural Language Processing project that was tasked to improve AI’s understanding of users’ requests. I monitored, improved the quality of the linguistic data used for training the Machine Learning model, and fixed any linguistic errors in the data to increase the accuracy of the model’s prediction.
Since last year, I have been on multiple trainings on data science and machine learning, including the Professional Diploma in Machine Learning and AI program. Along with my data and machine learning role, I'm currently working part-time on my early-stage startup project of a mobile and online marketplace platform for a peer-to-peer lending service.
Skills
Programming Languages:
Natural Language Processing:
Other Tools:
Data Science and Machine Learning:
Languages:
THAI
ENGLISH
JAPANESE
GERMAN
Native
B1 - Intermediate
B1 - Intermediate
C1 - Fluent
Natural Language Processing
The housing crisis becomes a topic creating concerns for many people around the world mostly due to a lack of housing that is affordable to purchase or rent. Referring to the World Bank’s statement, 1.6 billion people tend to be impacted by the global housing shortage by 2025. Learning opinions relating to housing from society in a most recent and constant manner will lead to new insights into housing issues. This paper has an objective to study the sentiment of tweets about housing as well as the top-mentioned keywords of the tweets by using the Natural Language Processing technique, which some challenges encountered from the sentiment analysis will also be introduced.
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