# Deep Learning-Based Sentimental Analysis for Large-Scale Imbalanced Twitter Data
## 2 Literature Review  
Previously, many researchers used manual annotation methods to assess the accuracy of their models. It is quite a hard and time-consuming job that requires high energy and effort. Secondly, each researcher thinks in a different way to investigate the text. Later, text analysis researchers used NLP techniques to classify text automatically based on semantics. The hashtag is considered a useful feature to automate the labelling and classification process [14; 15]. There are 50,000 tweets used with hashtags divided into six emotions such as anger, disgust, fear, happy, sad, and surprised. A lexicon database is designed that contains these emotions used for the analysis of unseen tweets. It has two basic constraints; for example, those words which do not co-occur with corresponding tags will be eliminated. Secondly, there are many domains in which hashtags are not used to show the individual's emotions. Hence, this idea is a domain-specific [16; 17]. The knowledge graphs showed a vital contribution to data training using the machine and deep learning-related tasks. In [18], six basic emotions are proposed that are used to define the facial expressions. The Profile of Mood States (POMS) is a psychological device that shows the mood states in six different dimensions. A number of emotional adjectives are merged to present each mood dimension, i.e., sweet, bitter, anger, happy, etc. The method is suggested to be better depending on how it reads the strength of each emotion in the last month [19; 20].  
Several data mining techniques have emerged recently that are used to extract useful patterns from tweets. In [21], the sentimental verification of tweets is a challenging task due to multilingual text messages. The preprocessing steps are used to extract the tokens from the emotional dataset, and then graph propagation algorithm is used to zoom the contribution of each token in terms of similarity. Next, the sentimental analysis method is applied to classify each token's emotions. In [22], the data mining technique is used for the sentimental analysis of tweets data. The designed algorithm is used to classify each tweet in terms of emotions in real-time. The dataset is taken in this experiment is very small, and the algorithm does not work for a large dataset of tweets. Moreover, the data imbalance issue is not targeted in the proposed methodology. The geolocation tweets are classified based on their text. The sentimental analysis algorithm is applied to predict geolocation from tweet analysis due to differences in emotional states across different countries [23].  
Previously, most researchers targeted only one emotion classification. Also, the data imbalance and sparsity features problem is not tackled before applying classification algorithms. Our proposed hybrid approach mainly solved the problem of a large scale of tweets preprocessing, data imbalance, and highly correlated data problems, which are mostly present in tweet datasets. Moreover, the designed approach answers the following questions:  
* Can we label the sparse and incomplete tweet messages?
* Can we solve the imbalanced data problem in sparse tweet dataset?
* Can deep learning algorithm give better accuracy for large scale of tweets data?
* Can we retune the deep learning algorithm to get the optimal solution?