of document classification by making use of both the views and a large number of unlabeled documents. People express their emotions online using social media through tweets, questions and news headlines. These user emotions can be classified and used in applications such as sentiment retrieval and opinion summarization. [PERSON] et al. (2018) proposed an algorithm which introduces a multi-label maximum entropy (MME) model for emotion classification over short text. Rich features are generated by MME which models multiple emotion labels and valence scored by numerous users. L-BFGS (Limited-memory Broyden-Fletcher-Goldfarb-Shanno) algorithm is used along with MME to improve the robustness of the proposed method on varied-scale corpora. The user emotion classification results using MME shows an effective performance on real-world short text collections. Clustering, also known as unsupervised learning ([PERSON] et al., 2016), is the process of aligning objects into groups such that all similar data instances lie in the same group. Objects in the same cluster must have high intra-class similarity and low inter-class similarity. All existing clustering algorithms use distance measures ([PERSON] et al., 2016) to group the similar objects, these algorithms can cluster only numeric data and also the number of clusters must be known beforehand. Since the class labels of the online data are not known, and no prior knowledge about the clusters, the task of clustering becomes challenging. Incremental Learning or online learning ([PERSON] et al., 2016; [PERSON] et al., 2018; [PERSON] et al., 2018) aims to adapt the training models according to the arriving data without forgetting the acquired knowledge. Incremental Learning transfers the acquired knowledge from different batches to classify the test data. It helps to grow the network capacity by overcoming the problem of catastrophic interference ([PERSON] et al., 2016) i.e., successive training of each batch of newly arrived data causes the network to forget the previously acquired knowledge partially or completely. This type of learning is applied to the data streams ([PERSON] et al., 2016) to provide accurate results addressing the issues of, (1) limited computational resources such as memory and time and (2) the phenomenon of concept drift ([PERSON] et al., 2014) (changes in the distribution of data which occurs in a stream over time). Figure 1 shows an example of incremental learning. The existing clustering algorithms face a challenge in handling the requirements concurrently in the case of large, high-dimensional, unstructured data due to the time complexity involved. All these algorithms use the distance metric to compute the similarity between objects and produce an arbitrary clustering result depending on the initial setup. Since these algorithms can handle only structured data,  
Figure 1: Example of Incremental Learning. a new clustering algorithm _uCLUST_[12] has been designed to overcome the above challenges and cluster unstructured big data. Extreme Learning Machine(ELM) is a feedforward neural network [13; 14] which randomly chooses input weights and creates a model for classifying test data samples using a single hidden layer. A discriminative clustering approach via ELM [15] is proposed by [PERSON] et al., which is an unsupervised learning framework and extends the classification rules into clustering. Three discriminative clustering approaches were designed, (1) weighted ELM (W-ELM) classifier which gradually maximizes the discrimination of the data and (2) the other two methods were based on [PERSON]'s Linear Discriminant Analysis (LDA). Among the two methods, one follows alternative optimization and the other adopts kernel k-means approach for clustering. This paper proposes a framework _CUIL_ which adopts Extreme Learning Machine (ELM) neural networks to implement the incremental learning. After clustering the unstructured data using _uCLUST_, the created clusters are fed into ELM to create a model for each subset arriving over time.