# Short Text Clustering Algorithms, Application and Challenges: A Survey
## 3 Components of Short Text Clustering
### Wikipedia
#### 3.2.2 Deep Learning Measures  
Deep learning is currently the undisputed best technology for supervised machine learning, particularly for numerical data classification and clustering. However, its use in unsupervised learning has been more limited and recent [97]. Recently, deep learning has been used for unsupervised tasks, including topic modelling and clustering [98]. In many cases, the training goals are still the same, and deep learning appears to be most helpful with feature extractors such as convolutional neural network (CNN) [99]. The process of transforming input data into a collection of features is known as feature extraction [99]. Feature extraction is a technique used in machine learning to improve the efficacy of learning algorithms by transforming training data and augmenting them with extra features to make machine learning algorithms much more adequate. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Methods** & **Advantage** & **Disadvantage** \\ \hline BOW & It is simple and widely used. & It ignores syntactic and semantic relationships between words and leads to sparsity. \\ \hline VSM & It is simple and effective. & It has trouble distinguishing between synonyms and polysemy. \\ \hline LDA & Simplicity and effectiveness led to widely used. & It disregards the sequence of the words in a sentence and the multiple meanings of words. \\ \hline DMM & It can obtain the representative words of each cluster. & It assumes that there is only one topic covered in each document. \\ \hline LSA & It can distinguish between synonyms and polysemy and take semantic relationships among the concepts to find relevant documents. \\ \hline Word2 Vec & It can process semantic information quickly. & It ignores the order of words in a sentence. \\ \hline Doc2 Vec & It analyses word order and trains & It ignores polysemy and synonyms of words. \\  & different-length texts. & It cannot retain the memory relationship between words and words and is faster in training. \\ \hline Glove & it preserves the regular linear pattern between words and words and words and is faster in training. & It cannot retain the memory relationship between words and words. \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Advantages and disadvantages of non-deep-learning measures. Deep learning is one of several strategies utilized for short text. Recently, short text has grown on social media platforms, where people can share information and assemble societal opinions through short text conversation. The short text data comprise sparse word co-occurrences; it is challenging for unsupervised text mining to uncover categories, concepts or subjects within the data [89]. During the last few years, deep learning methods have shown much power to extract features autonomously and automatically from raw data [100]. In general, a deep learning model is constructed of many layers of neural networks. Each layer comprises numerous basic signal-processing units known as neurons. The basic structure of neurons is depicted in Figure 12.  
A neuron can take an input signal and produce an output signal using the neuron's strategies it has learned. Raw information is gradually processed as it passes through multiple interconnected layers of neurons. The structure of a multi-neuron neural network is illustrated in Figure 13. The artificial neuron network uses a massive scale of basic units to handle input in a similar way that the human brain does. Recently, deep text representation has been learned using supervised deep learning techniques [25], depending on shallow-to-deep auto-encoders utilizing recurrent neural networks (RNNs) [101], CNNs [101], long short term memory (LSTM), bidirectional long short term memory (Bi-LSTM) and recursive tree LSTM [102]. Nevertheless, in many applications, a dense representation should be discovered in an unsupervised fashion to identify clusters, concepts or topics in the short text. Two elementary procedures, convolution and pooling, form the basis of deep neural network models.