Related Papers
Text Analytics with Python A Practical Real-World Approach to Gaining Actionable Insights from Your Data — Dipanjan Sarkar
Anand Trivedi
Computational Linguistics
Natural Language Processing with Python Steven Bird, Ewan Klein, and Edward Loper (University of Melbourne, University of Edinburgh, and BBN Technologies) Sebastopol, CA: O'Reilly Media, 2009, xx+482 pp; paperbound, ISBN 978-0-596-51649-9, $44.99; on-line free of charge at nltk.org/book
2010 •
Michael Elhadad
Text Analytics for Big Data
Sharvari C Tamane
Most of the data used in various application areas like government, business and research is available in the form of text and therefore it is the requirement of these applications that it should derive high quality information by converting text into data for analysis purpose. The process of deriving high-quality information from the text is known as text analytics. Text analytics techniques represent knowledge, facts, business rules and relationships which are otherwise available in textual form incomprehensible for automatic processing. This paper mainly explores on how the different types of unstructured data are analyzed to get real meaning from data and which different text analytics tools are available for big data infrastructure. Routinely statistical and natural language processing techniques are used in text analytics to retrieve information from unstructured data. The idea behind this type of analytics is to determine who did what to whom, when, where, how and why. This information is then combined with structured information available in the data warehouse using various tools to gather further insight. At the end an overview of some of the players of this market is provided.
Journal of Applied Information Science
Text Analytics Framework using Apache Spark and Combination of Lexical and Machine Learning Techniques.pdf
2016 •
Publishing India Group, padma d
Today, we live in a ‘data age’. The sudden increase in the amount of user-generated data on social media platforms like Twitter, has led to new opportunities and challenges for companies that strive hard to keep an eye on customer reviews and opinions about their products. Twitter is a huge fast emergent micro-blogging social networking platform for users to express their views about politics, products sports etc. These views are useful for businesses, government and individuals. Hence, tweets are used in this framework for mining public’s opinion. Sentiment analysis is a process of naturally recognising whether a user-generated content expresses positive, negative or neutral opinion about an entity (i.e. product, people, topic, event etc). The traditional analytics tools are costly and are not built to handle Big data. Hadoop, though being a popular framework for data intensive applications, does not perform well on iterative process (like data analysis) due to the cost paid for data reloading from disk for each iteration. This paper proposes a text analysis framework for twitter data using Apache spark and hence is more flexible, fast, and scalable. The proposed framework is also domain independent as it uses a hybrid approach by combining supervised machine learning algorithms (Naïve Bayes and decision tree machine learning algorithms) and lexicon approach (pattern analyser) for sentiment classification thereby comparing various supervised learning models and using the one with highest accuracy for predicting sentiment.
Proceedings of SW20 The OR Society Simulation Workshop
Text Analytics for Simulation with Python
2020 •
Roger McHaney
Natural Language Processing with Python Steven Bird 2009
Isromi Janwar
Text Analytics A business guide
Jessica Oliveira
Education for Information
Learning text analytics without coding? An introduction to KNIME
Jukka Tyrkkö
The combination of the quantitative turn in linguistics and the emergence of text analytics has created a demand for new methodological skills among linguists and data scientists. This paper introduces KNIME as a low-code programming platform for linguists interested in learning text analytic methods, while highlighting the considerations necessary from a linguistics standpoint for data scientists. Examples from an Open Educational Resource created for the DiMPAH project are used to demonstrate KNIME’s value as a low-code option for text analysis, using sentiment analysis and topic modelling as examples. The paper provides detailed step-by-step descriptions of the workflows for both methods, showcasing how these methods can be applied without writing code. The results suggest that visual or low-code programming tools are useful as an introduction for linguists and humanities scholars who wish to gain an understanding of text analytic workflows and computational thinking. However, as...
Proceedings of the Annual Hawaii International Conference on System Sciences
Introduction to the Minitrack on Text Analytics
2021 •
Normand Péladeau
Text Analytics: the convergence of Big Data and Artificial Intelligence
Antonio Moreno Sandoval, Teófilo Redondo
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 analyze millions of emails; you can analyze 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 Dataanalysis. Text Analytics has a number of subdivisions: Information Extraction, Named Entity Recognition, Semantic Web annotated domain’s representation, and many more. Several techniques are currently used and some of them have gained a lot of attention, such as Machine Learning, to show a semisupervised enhancement of systems, but they also present a number of limitations which make them not always the only or the best choice. We conclude with current and near future applications of Text Analytics. Keywords— Big Data Analysis, Information Extraction, TextAnalytics
