Abstract: Sentiment analysis and opinion mining have become
emerging topics of research in recent years but most of the work
is focused on data in the English language. A comprehensive
research and analysis are essential which considers multiple
languages, machine translation techniques, and different classifiers.
This paper presents, a comparative analysis of different approaches
for multilingual sentiment analysis. These approaches are divided
into two parts: one using classification of text without language
translation and second using the translation of testing data to a
target language, such as English, before classification. The presented
research and results are useful for understanding whether machine
translation should be used for multilingual sentiment analysis or
building language specific sentiment classification systems is a better
approach. The effects of language translation techniques, features,
and accuracy of various classifiers for multilingual sentiment analysis
is also discussed in this study.
Abstract: Prediction of future research topics by using time series analysis either statistical or machine learning has been conducted previously by several researchers. Several methods have been proposed to combine the forecasting results into single forecast. These methods use fixed combination of individual forecast to get the final forecast result. In this paper, quite different approach is employed to select the forecasting methods, in which every point to forecast is calculated by using the best methods used by similar validation dataset. The dataset used in the experiment is time series derived from research report in Garuda, which is an online sites belongs to the Ministry of Education in Indonesia, over the past 20 years. The experimental result demonstrates that the proposed method may perform better compared to the fix combination of predictors. In addition, based on the prediction result, we can forecast emerging research topics for the next few years.