Text Analytics And NLP
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- Text Analytics And NLP
Text Analytics And NLP
Considering the existence of large volume of data on the web, it has been always seen as an issue that the search engines sometimes provide related data but it can also not be denied that sometimes the data given is not related to the search made by user. Thus it can be asserted that it often becomes difficult to access the related data and since the data available on web is mostly unstructured, it is time-consuming to retrieve the related data (Ergün, 2017). In order to optimize the process of accessing data and processing there have been proposed or implemented a large number of technologies. Some of the key technologies are natural language processing (NLP), text analytics, speech recognition etc.
Natural language processing can be defined as an important area of computer science, artificial intelligence and computation linguistics which are associated with processing the human languages. While NLP aims at processing the text, text analytics is associated with retrieving or obtaining relevant information from the searched or unorganized text. Thus, text analytics can be defined as a type of data analysis enabled using NLP methodologies.
The evolution of NLP took place several years ago and it includes several methods for processing text (Kao & Poteet, 2005). Some of these methods or approaches are:
Stemming: This approach aims at removing suffixes from the text.
Lemmatization: This approach aims at substituting the inflected word.
Multi-word phase grouping, etc.
NLP can be asserted as a division of artificial intelligence which is based on algorithms for automatic representation and processing of different forms of natural language, consequently facilitating communication with computer and humans (Kalyanathaya, Akila, & Rajesh, 2019).
Contrary to NLP, text analytics has been in trend recently. It is associated with the techniques that are employed in the field of text mining, information retrieval etc. Predominantly, the objective of text analytics is to obtain certain relevant text from the large volume of irrelevant text available across the web. The most common form of text analytics is text retrieval which is generally performed by the search engines. In addition the text analytics is also associated with classifying text as per certain defined categories. The text retrieval tasks generally employ techniques of NLP discussed previously. It is related with identifying certain defined patterns in natural language texts. In order to analyze these natural language texts, there are used several techniques, as depicted in the works performed for natural language processing (Jurafsky & Martin, 2008).
It has been reported by some researchers that text analytics is one of the simplest means to obtain relevant data from the large volumes of data is text analytics (Pejić Bach, Krstić, Seljan, & Turulja, 2019). Obtaining unrelated data from the web and converting it into relevant information in order to enable decision-making process (Pejić Bach et al., 2019).
Though applied on unstructured text, the primary function is to arrange or structure text in order to analyze the text. Similarly, the authors (Liu, Fei, Hou, Zhang, & Sun, 2007) asserted that the approach aims at extracting patterns from different text documents. With the similar motive, the authors (Yehia1, Ibrahim, & Abulkhair, 2016). Reported about certain techniques such as classification, information extraction, clustering of topics etc, sentiment analysis (Nakayama & Wan, 2017; Schumaker, Zhang, & Chun Neng Huang, 2012), keyword extraction (Ong, Chen, Sung, & Zhu, 2015), NLP (Klopotan, Zoroja, & Meško, 2018) etc.
Employed text mining for extracting topics using content analysis of e-commerce organizations while the authors (Reyes-Menendez, Saura, & Alvarez-Alonso, 2018) used it for social media and categorizing them as per the sentiments.Recently, the vast amount of data is termed under a common term, Big Data. This data is usually unstructured and is associated with commercial domain (Marriott, 2015). This has attracted the attention of researchers towards text analytics. It has been described as an extended version of data mining by some researchers since it performs the task of retrieving related task from the unrelated sources of text.
It is also known as Intelligent Text Analysis since it intelligently extracts the structured data from the unstructured one as desired or searched by the user. Thus, text analytics can be defined as a wide field which is associated with obtaining information from unstructured or even semi-structured data sets. Some of the examples of unstructured data sets are emails, blogs, articles etc. It is also noteworthy that in text analytics exhibits similarity with data mining in almost all cases. However, there exists a difference that while text analytics tools extract structured information from unstructured one, the data mining tools enable handling structured data from databases.
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