Abstract
Most sentiment analysis researches are done with the help of supervised machine learning techniques. Analyzing sentiment for these English text reviews is a non-trivial task in order to gauge public perception and acceptance of a particular issue being addressed. Nevertheless, there are not many studies conducted on analyzing sentiment of Malay news headlines due to lack of resources and tools. The Malay news headlines normally consist of a few words and are often written with creativity to attract the readers’ attention. This paper proposes a standard framework that investigates factors affecting sentiment prediction of Malay news headlines using machine learning approaches. It is important to investigate factors (e.g., types of classifiers, proximity measurements and number of Nearest Neighbors, k) that influence the prediction performance of the sentiment analysis as it helps to study and understand the parameters that can be tuned to optimize the prediction performance. Based on the results obtained, Support Vector Machine and Naïve Bayes classifiers were capable to obtain higher accuracy compared to the k-Nearest Neighbors (k-NN) classifier. In term of proximity measurement and number of Nearest Neighbors, k, the k-NN classifier achieved higher prediction performance when the Cosine similarity is applied with a small value of k (e.g., 3 and 5), compared to the Euclidean distance because it measures can be affected by the high dimensionality of the data.
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Alfred, R., Yee, W.W., Lim, Y., Obit, J.H. (2016). Factors Affecting Sentiment Prediction of Malay News Headlines Using Machine Learning Approaches. In: Berry, M., Hj. Mohamed, A., Yap, B. (eds) Soft Computing in Data Science. SCDS 2016. Communications in Computer and Information Science, vol 652. Springer, Singapore. https://doi.org/10.1007/978-981-10-2777-2_26
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DOI: https://doi.org/10.1007/978-981-10-2777-2_26
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