Capital Markets Prediction: Multi-Faceted Sentiment Analysis using Supervised Machine Learning

Kelebeng, Kushatha; Hlomani, Hlomani

doi:10.14257/ijdta.2017.10.6.07

Cited by 2 publications

(1 citation statement)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…-Predicting Botswana Stock Exchange using supervised machine learning (Kelebeng & Hlomani, 2017a;2017b) Land change intensities were examined at the time interval, category, and transition levels using Intensity Analysis. A combination of multi-layer perceptron neural network and Markov chain analysis was used to project LULC to 2028 and investigate future changes.…”

Section: Nyonimentioning

confidence: 99%

Glimpse into Botswana’s AI Readiness Landscape

Shonhe,

Kolobe

2023

JeDEM

View full text Add to dashboard Cite

This study seeks to provide insights into Botswana’s AI readiness landscape. It was achieved by analysing secondary data from the Oxford Insights 2022- Government AI Readiness Index (AIRI). According to the AIRI, Botswana is in position 98, out of 181 countries surveyed. The major drawbacks to successful AI adoption were; a lack of AI strategy, limited capacity to support change, an immature technology sector incapable of supporting innovation, inadequate skills to support AI development, insufficient technological infrastructure to support AI, insufficient data to train AI models, and there are few use cases identified in the public sector. Despite these hurdles, the country is putting in efforts to transform digitally, and there are opportunities for improvement. The country is faring similarly, or even better than, regional peers but is lagging behind global peers in the upper middle-income group. Consequently, it is recommended that the government should start by developing an AI strategy to set the vision for AI adoption.

show abstract

Section: Nyonimentioning

confidence: 99%