The continuous rapid growth of electronic Arabic contents in social media channels and in Twitter... more The continuous rapid growth of electronic Arabic contents in social media channels and in Twitter particularly poses an opportunity for opinion mining research. Nevertheless, it is hindered by either the lack of sentimental analysis resources or Arabic language text analysis challenges. This study intro‐ duces an Arabic Jordanian twitter corpus where Tweets are annotated as either positive or negative. It investigates different supervised machine learning senti‐ ment analysis approaches when applied to Arabic user's social media of general subjects that are found in either Modern Standard Arabic (MSA) or Jordanian dialect. Experiments are conducted to evaluate the use of different weight schemes, stemming and N-grams terms techniques and scenarios. The experi‐ mental results provide the best scenario for each classifier and indicate that SVM classifier using term frequency–inverse document frequency (TF-IDF) weighting scheme with stemming through Bigrams feature outperforms the Naïve Bayesian classifier best scenario performance results. Furthermore, this study results outperformed other results from comparable related work.
The study tries to find out the most important features in building
games based on the grossing. ... more The study tries to find out the most important features in building games based on the grossing. The study is limited to fifty iPhone games that have achieved top grossing in the USA. The game features were extracted from a previous study [1] and classified through ARM funnel into five groups (“A”, “R”, “M”, “AR”, and “RM”). The paper follows CRISP-DM approach under SPSS Modeler through business and data understanding, Data preparation, model building and evaluation. The researcher uses Decision Tree model since the features have closed value i.e. (Yes/No) on the grossing weight. The study reached to the most important 10 features out of 31. These features are important to build successful mobile games. The study emphasizes on the vailability of (Acquisition, Retention and Monetization) elements on every uccessful game and if any is missed, will lead to the failure of the game.
The continuous rapid growth of electronic Arabic contents in social media channels and in Twitter... more The continuous rapid growth of electronic Arabic contents in social media channels and in Twitter particularly poses an opportunity for opinion mining research. Nevertheless, it is hindered by either the lack of sentimental analysis resources or Arabic language text analysis challenges. This study intro‐ duces an Arabic Jordanian twitter corpus where Tweets are annotated as either positive or negative. It investigates different supervised machine learning senti‐ ment analysis approaches when applied to Arabic user's social media of general subjects that are found in either Modern Standard Arabic (MSA) or Jordanian dialect. Experiments are conducted to evaluate the use of different weight schemes, stemming and N-grams terms techniques and scenarios. The experi‐ mental results provide the best scenario for each classifier and indicate that SVM classifier using term frequency–inverse document frequency (TF-IDF) weighting scheme with stemming through Bigrams feature outperforms the Naïve Bayesian classifier best scenario performance results. Furthermore, this study results outperformed other results from comparable related work.
The study tries to find out the most important features in building
games based on the grossing. ... more The study tries to find out the most important features in building games based on the grossing. The study is limited to fifty iPhone games that have achieved top grossing in the USA. The game features were extracted from a previous study [1] and classified through ARM funnel into five groups (“A”, “R”, “M”, “AR”, and “RM”). The paper follows CRISP-DM approach under SPSS Modeler through business and data understanding, Data preparation, model building and evaluation. The researcher uses Decision Tree model since the features have closed value i.e. (Yes/No) on the grossing weight. The study reached to the most important 10 features out of 31. These features are important to build successful mobile games. The study emphasizes on the vailability of (Acquisition, Retention and Monetization) elements on every uccessful game and if any is missed, will lead to the failure of the game.
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Papers by Khaled Mohammad Alomari
games based on the grossing. The study is limited to fifty iPhone games that have achieved top grossing in the USA. The game features were extracted from a previous study [1] and classified through ARM funnel into five groups (“A”, “R”, “M”, “AR”, and “RM”). The paper follows CRISP-DM approach under SPSS Modeler through business and data understanding, Data preparation, model building and evaluation. The researcher uses Decision Tree model since the features have closed value i.e. (Yes/No) on the grossing weight. The study reached to the most important 10 features out of 31. These features are important to build successful mobile games. The study emphasizes on the vailability of (Acquisition, Retention and Monetization) elements on every uccessful game and if any is missed, will lead to the failure of the game.
games based on the grossing. The study is limited to fifty iPhone games that have achieved top grossing in the USA. The game features were extracted from a previous study [1] and classified through ARM funnel into five groups (“A”, “R”, “M”, “AR”, and “RM”). The paper follows CRISP-DM approach under SPSS Modeler through business and data understanding, Data preparation, model building and evaluation. The researcher uses Decision Tree model since the features have closed value i.e. (Yes/No) on the grossing weight. The study reached to the most important 10 features out of 31. These features are important to build successful mobile games. The study emphasizes on the vailability of (Acquisition, Retention and Monetization) elements on every uccessful game and if any is missed, will lead to the failure of the game.