# Enhancement of Text Analysis Using Context-Aware Normalization of Social Media Informal Text
## 5 Conclusions  
In this paper, we simulated a new method for text analysis of informal/abbreviated text data using the deep-learning-based text classification model integrated with the proposed TVH. We used two state-of-the-art text analysis models, i.e., LSTM-based text classification, and BERT-based text classification. We proposed a new context-aware generic normalization method to handle noise in SM text without losing useful information in this text. The proposed TVH handles a wide range of SM textual variations. These variations include word-to-phrase/sequence transformation, and word-to-word transformation. The word-to-sequence normalization is used to tackle user-generated abbreviations, dialectal usage, and word-deletion problems in the SM text. The word-to-word normalization include typos, word enlargement, short words and phonetic similarity-based OOV words handling. The performance of the text analysis is inversely proportional to the fraction of noise in the data. The more the noise in the data the poorer the text analysis performed. BERT performs better than the LSTM-based text analysis in all cases. However, there is room for improvement in both cases, depending on the fraction of noise. From simulations, we observed that the proposed TVH can be used as a pre-processing tool to improve the performance of the text classification significantly. In our experiments we observed that the average improvement is higher in the case of noisier data. We compare the performance of the proposed text analysis pipeline with the existing state-of-the-art text analysis methods to observe the performance when noise is removed from the SM text. We observed from simulation that the performance of the text analysis methods is improved when integrated with the proposed normalization scheme. The average improvement in the performance of the proposed text analysis pipeline was up to 6% where the maximum improvement was about 12% in the case of very noisy textual data. As we know, the existing state-of-the-art text analysis methods are based on deep learning, which are data-dependent and data-driven approaches. Therefore, the performance of the text analysis depends on the fraction of noise in the text data. We observed that the text analysis methods performed well on the text with less textual variations; however, in the case of high-variation data the performance is poor. We applied the proposed model to different text analysis applications such as tweet classification, review analysis, spam detection, cyberbullying detection, news article classification and COVID-19 apps review classification. The proposed model is generic as it performs well in different applications scenarios. From experiments, we found that the performance of our proposed model was superior in the case of informal/abbreviated social network text data than the existing methods. Our system was able to tackle different variants of text data to improve the performance of the text analysis models. Our system correctly identified most of the alternatives of the text, and it updated the dictionary used in this work for future occurrences of these words, which minimizes the normalization overhead.