Abstract: The analysis of the text content in emails, blogs, tweets, forums and other forms of textual communication constitutes what we call text analytics. Text analytics is applicable to most industries: it can help analyse millions of emails; you can analyse customers’ comments and questions in forums; you can perform sentiment analysis using text analytics by measuring positive or negative perceptions of a company, brand, or product. Text Analytics has also been called text mining, and is a subcategory of the Natural Language Processing (NLP) field, which is one of the founding branches of Artificial Intelligence, back in the 1950s, when an interest in understanding text originally developed. Currently Text Analytics is often considered as the next step in Big Data analysis. Text Analytics has a number of subdivisions: Information Extraction, Named Entity Recognition, Semantic Web annotated domain’s representation, and many more. This work provides a detailed study on text analytics based on different type of deep learning techniques.
Keywords: Big Data Analysis, Information Extraction, Text Analytics, Deep Learning etc.
| DOI: 10.17148/IJARCCE.2020.9805