# Meta-Learner for Amharic Sentiment Classification
## 5 Conclusions  
The main purpose of this study is to investigate building a stacking strategy with a meta-learner using TF-IDF character (1,7) gram feature sets (or using SMOTE) with a CV of five folds for Amharic sentiment classification relying on base learners (i.e., SVM, NB, and RF) across four datasets. Finally, we found that the suggested stack model outperformed the base classifiers for sentiment classification of user-generated text in social media using character/word \(n\)-grams features with and without application of the SMOTE technique. In conclusion, employing SMOTE approaches to balance datasets using TF-IDF character/word \(n\)-grams features increased the performance of stack classifiers across datasets when compared to base learners. Of all the individual base learners, SVM has achieved the highest performance across all the data sets compared to the other base learners. As SVM involves the cost function parameter, which regulates the balance between bias and variance by tuning it depending on the training set, SVM also has a kernel function which maps non-separable data into linear or nonlinear separable problem relying on distance maximization. RF has better performance than NB as RF considers not only the cost criterion function, but it also has an ensemble of multiple tree learners. In contrast, NB performed the worst on all the data sets. One of the reason for this is that NB has the shortcoming of independence assumption; this means it assumes features are independent, which is not the case, specifically for the character/word \(n\)-grams features. The key contribution of this research is as follows: (i) using character \(n\)-grams, the stacking of base classifiers with meta-learners has improved the performance of sentiment classification across all datasets compared to the base learners; (ii) using character/word \(n\)-grams with and without SMOTE procedure, the stack classifier has also performed better than the base classifiers' performance across all the datasets; and (iii) as stacking classifier is a combination of heterogeneous classifiers, it has also performing better than a combination of homogeneous classifiers. Below is the highlights of the answers for the corresponding research questions raised in Section 1. * (1) To what extent does ensemble learning improve Amharic sentiment classification on a small set of user-generated texts compared to base learners? The answer for this research question is reported all four experimental settings (i.e., Experiment I-IV), where its results are reported in Table 4. That means, the proposed stack ensemble model outperforms the individual learners on all sentiment classification data sets using TF-IDF character (1, 7) gram with and without application of SMOTE shown in Table 4, as compared to the data sets using TF-IDF uni-gram with and without SMOTE balancing technique. The proposed stack learner has achieved a rise of accuracy ranging from 10% to 31% over all base learners in the above experimental settings. * (2) Which feature representation (TF-IDF uni-gram, TF-IDF character \(n\)-grams) has better performance of Amharic sentiment classification with the proposed ensemble approach? The answer for this research question is provided in Table 4 that reveals ensemble learner outperforms on TF-IDF character \(n\)-grams over TF-IDF word uni-gram. * (3) Does SMOTE technique improve performance of the proposed approach by balancing the imbalanced labeled user generated data? The answer to this research question is reported in Table 4, where SMOTE has significantly improved sentiment classification of ensemble learners and base learners when it is applied with both TF-IDF character \(n\)-grams and TF-IDF word uni-gram feature sets. * (4) On which feature representation of Amharic texts does SMOTE show higher performance improvement of sentiment classification? The answer is provided in Table 4 showing that SMOTE boosts sentiment classification when it applied to TF-IDF character \(n\)-grams.