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The generation of texts are dramatically increased in this era. A text basically consists of structured and unstructured texts. The enormous amount of unstructured texts can be easily perceived by humans, unfortunately cannot be simply processed by computer. It needs efficient techniques to reduce the information into more valuable vectors. In this...
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Context 1
... selected words from three English datasets were translated into Malay. Table 2 shows the translation of dataset from English to Malay. As seen in Table 2, the English dataset was translated to Malay using Online Dictionary: Cambridge Dictionary (https://dictionary.cambridge.org/) ...Context 2
... 2 shows the translation of dataset from English to Malay. As seen in Table 2, the English dataset was translated to Malay using Online Dictionary: Cambridge Dictionary (https://dictionary.cambridge.org/) dan Kamus Oxford Fajar (Hawkins, 2006). ...Context 3
... selected words from three English datasets were translated into Malay. Table 2 shows the translation of dataset from English to Malay. As seen in Table 2, the English dataset was translated to Malay using Online Dictionary: Cambridge Dictionary (https://dictionary.cambridge.org/) ...Similar publications
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Citations
... These methods capture the lexical features of the text and are simple to implement; however, they ignore the semantic and syntactic features of the text. To address this issue, several studies have expanded and enriched the context of data from an ontology [68,69] or Wikipedia [70,71]. However, these techniques require a great deal of understanding of NLP. ...
The number of online documents has rapidly grown, and with the expansion of the Web, document analysis, or text analysis, has become an essential task for preparing, storing, visualizing and mining documents. The texts generated daily on social media platforms such as Twitter, Instagram and Facebook are vast and unstructured. Most of these generated texts come in the form of short text and need special analysis because short text suffers from lack of information and sparsity. Thus, this topic has attracted growing attention from researchers in the data storing and processing community for knowledge discovery. Short text clustering (STC) has become a critical task for automatically grouping various unlabelled texts into meaningful clusters. STC is a necessary step in many applications, including Twitter personalization, sentiment analysis, spam filtering, customer reviews and many other social network-related applications. In the last few years, the natural-language-processing research community has concentrated on STC and attempted to overcome the problems of sparseness, dimensionality, and lack of information. We comprehensively review various STC approaches proposed in the literature. Providing insights into the technological component should assist researchers in identifying the possibilities and challenges facing STC. To gain such insights, we review various literature, journals, and academic papers focusing on STC techniques. The contents of this study are prepared by reviewing, analysing and summarizing diverse types of journals and scholarly articles with a focus on the STC techniques from five authoritative databases: IEEE Xplore, Web of Science, Science Direct, Scopus and Google Scholar. This study focuses on STC techniques: text clustering, challenges to short texts, pre-processing, document representation, dimensionality reduction, similarity measurement of short text and evaluation.
