# Enhancement of Text Analysis Using Context-Aware Normalization of Social Media Informal Text
## 4 Results and Analysis
### Simulation Results  
We simulated the proposed text analysis pipeline to evaluate and compare its performance with the existing text analysis methods. From simulations, we observed that the proposed text analysis pipeline performs better than the previous methods in the case of noisy/informal SN data. For performance evaluation of the proposed sentiment analysis strategy, we used accuracy, average precision (\(AP\)), average recall (\(AR\)), F1-score, and Accuracy. The average precision (\(AP\)) is the sum of precision (\(P_{ci}\)) of all classes divided by number of classes (\(N\)) in the dataset as shown in Equation (1). \[AP=\frac{\sum_{i=1}^{N}P_{ci}}{N} \tag{1}\]  
The average recall (\(AR\)) is the mean of the recall (\(R_{ci}\)) for all classes, as shown in Equation (2). \[AR=\frac{\sum_{ci=1}^{N}R_{ci}}{N} \tag{2}\]  
where \(P_{ci}\) and \(R_{ci}\) are the precision and recall of class \(ci\), respectively. The precision is defined as the ratio of true prediction of class \(ci\) to the number of all predictions of class \(ci\). \[P_{ci}=\frac{TP_{ci}}{PR_{ci}} \tag{3}\]  
The recall is defined as the number of true predictions of class \(ci\) divided by the total number of occurrences of class \(ci\) in the dataset. \[R_{ci}=\frac{TP_{ci}}{T_{ci}} \tag{4}\]  
\(TP_{ci}\), \(PR_{ci}\), and \(T_{ci}\) are the true predictions, total predictions, and total number of elements of class \(ci\), respectively. In textual communication the text can be noisy or clean; however, the SM text is usually very noisy, due to its informal nature. We conducted simulations of four different cases for analysis of SM text. These cases include clean training and test (ideal case), noisy training and test (real case), clean training and noisy test (very poor performance), and clean training and preprocessed (TVH) test (proposed method and can be realized). For simulation we used the spam dataset. For the first case i.e., clean training and test, we used manually normalized (cleaned) dataset. We have introduced some errors manually in the spam dataset to make it noisy. In the second case we have used the noisy training and test data, while as a third case we used the manually normalized training data and the noisy test data for simulating. In the last case we trained the text analysis model using the manually normalized data and test data normalized using the proposed TVH. In Table 4, the results of different possible scenarios is shown. From simulation we observed that the results of our proposed text analysis pipeline are the closest to the ideal case. The advantage of our proposed method is its independence of the wide range of textual variations. In the existing methods, classification is done by learning from data. The existing techniques (noisy training and text) works well for the text with a narrow range of variations and unique texts. However, in SM text, there iswide range of textual variations and hence this method is not suitable for SM informal text analysis. We also observed an improvement in performance of the text normalization while tackling the wide range of OOV words with context consideration. For performance evaluation of spell-checking we used precision (\(P\)), recall (\(R\)), and F1-scores. The precision and recall are calculated using Equations (5) and (6), respectively. \[P=\frac{TC}{TC+WC} \tag{5}\]  
\[R=\frac{TC}{TC+NC} \tag{6}\]  
where \(TC\), \(WC\), and \(NC\) are true corrections, wrong corrections, and no corrections. We consider that for any misspelled word there exists a valid dictionary replacement word; therefore, we considered that no corrections \(NC\) as false negatives. Table 5 shows the comparison of the proposed TVH with the current normalization methods. From simulation we observed that the proposed normalization method outperformed the existing methods, due to the hybrid nature of noise handling and context awareness. The proposed TVH method is simulated on the spell-check dataset [83], and other manually collected data such as list of dialects, and list of abbreviated data.