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Abstract
Unstructured data generated from sources such as the social media and traditional text documents are increasing and form a larger proportion of unanalysed data especially in the developing countries. In this study, we analysed data received from the major print and non-print media houses in Uganda through the Twitter platform to generate non-trivial knowledge by using text mining analytics. We also explored the determinants of derived sentiments in Twitter messaging. The results show that sentiments generated from tweets derived from the main print media houses (Daily Monitor and New Vision) were positively correlated, so were the sentiments from the non-print media (NBS TV and NTV) for the study period. Most of the sentiments on topics of security, politics and economics were found to be negative, while those on sports were positive. Furthermore, the tweet sentiment statistical logistic model revealed that negative sentiments were determined by the retweet status, retweet count and source of the tweets. Moreover, the positive sentiments were determined by the topic of discussion, type of media house and other sources of tweets (p<0.05). Therefore, we recommend further extensions on the predictive statistical models to classify sentiments from social media based on the concept of big data analytics.
Highlights
Text or unstructured data comprise approximately 80% of the data generated from vast fields including business, research and life science [1]
Improvement in technology enhances increase in text databases and making the study of text mining a core field in data analysis because it deals with technologies of extracting new non-trivial knowledge from the huge textual datasets
It was observed that Thursday 2017/07/13 registered the highest number of tweets across most media houses including NTV, NBS and Daily Monitor
Summary
Text or unstructured data comprise approximately 80% of the data generated from vast fields including business, research and life science [1]. The nature of such data poses management and methodological challenges during analysis. If well handled it could be a vital source of knowledge for planning and decision making in many aspects [2]. Improvement in technology enhances increase in text databases and making the study of text mining a core field in data analysis because it deals with technologies of extracting new non-trivial knowledge from the huge textual datasets. All documents that are generated in the form of unstructured format contain useful information in its raw form. Due to its magnitude, this type of data has become a complex process for individuals to conduct summaries and statistical analysis for such information [3]
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