# Enhancement of Text Analysis Using Context-Aware Normalization of Social Media Informal Text
## 2 Background and Related Work  
Data presentation is an important task to retrieve accurate information from unsupervised text generated on SM. In most sentiment analysis approaches, pre-processing is applied for noise reduction in the SM text [15]. The most common pre-processing operations applied include POS tagging, stemming, short-word removal, stop-word removal, removing URLs, stemming, mentions substitutions, and acronym expansions The pre-processing of the SM text reduces noise in the data, and it improves the prediction accuracy and processing time of the text classification. [PERSON] et al. [16] investigated the effects of text pre-processing on the performance of text analysis in movie reviews. The experimental results significantly improve movie reviews' sentiment classification accuracy by using appropriate feature selection and representation after the text pre-processing. [PERSON] et al. [17] explored the effect of slang word pre-processing on Twitter sentiment analysis. They relied on the relationship of slang words with other co-existing words to observe the significance and opinion translation of the slang words due to other words. [PERSON] et al. [18] empirically explored the influence of various stop-word removal methods on tweet classification by applying various stop-word removal methods to different Twitter datasets. They evaluated the impact of stop-word removal based on the variations in data sparsity, feature space size, and classification accuracy. They observed that the use of the pre-compiled stop-word list affects performance negatively. [PERSON] et al. [19] observed a significant reduction in feature space due to text pre-processing. They reduced the word vocabulary size by 62% after pre-processing. However, these studies do not discuss the effects of pre-processing on the performance of Twitter text classification. [PERSON] et al. [20], and [PERSON] [21] investigated the effects of removing URLs, negation, stop words, repeated letters and numbers, acronym expansion, stemming, and lemmatization on the accuracy of Twitter sentiment classifiers. The experimental results show an increase in the accuracy of Twitter sentiment classifiers with negation replacement, acronym expansion, and repeated letter removal. However, it hardly changes with the removal of URLs, numbers, and stop words. [PERSON] et al. [15] provided a deep analysis of the impact of text pre-processing on Twitter text analysis considering various features models, different machine-learning classifiers on multiple Tweets datasets, in the multi-class classification task. The conventional pre-processing steps such as removing URLs, stop words, short words, stemming, and lemmatization only ensure the removal of unwanted and non-informative text from the SM text, and it groups similar words. However, it does not ensure complete cleaning and standardization of SM text. We use different pre-processing approaches to acquire clean processable data from noisy SM text and to handle noisy SM text efficiently for text analysis. These approaches include noisy text filtering [22], lexicon creation [23; 24], data-driven approaches [25], and text normalization [26]. The traditional text analysis method adopted the approach of filtering out noisy data prior to information retrieval from the SM text [22]. [PERSON] et al. [27] and [PERSON] et al. [28] provided methods to clean textual data. They provided an integrated method for textual data cleaning by combining different data cleaning techniques. [PERSON] et al. [29] presented an overview of the challenges of different textual data cleaning methods. The approach of text filtering out dwindles the size of textual data by removing the un-interpretable and non-sentiment text, leaving only the standard text (valid words). However, this may result in losing useful sentiment information due to filtering out the deliberately generated OOV words. Several manual lexicons have been prepared successfully for text analysis to overcome the problem of sentiment information loss.