This post, co-authored with my colleague Sophia Rowland, is the first of two focused on operationalizing text analytics with SAS. As businesses undergo digital transformation, a common priority is to discover new insights through harnessing text, the largest human-generated data source. SAS has been helping companies analyze their unstructured data for various industries and across a multitude of use cases for many years. Analysts tend to agree with our success as SAS was recently named a Leader in the Forrester Wave for AI-Based Text Analytics Platforms. However, as we often preach here at SAS, it's not just the modeling that's important.

Unstructured text is the largest human-generated data source, offering a wealth of insights for organisations able to uncover them. SAS helps businesses capitalise on the massive amounts of text data, discovering trends and opportunities that otherwise would have been missed. As a result, SAS was named a Leader in AI-based text analytics in two reports, The Forrester Wave: AI-Based Document-Focused Text Analytics Platforms, Q2 2020 and The Forrester Wave: AI-Based People-Focused Text Analytics Platforms, Q2 2020. "Unstructured text data offers access to an array of insights from which businesses can benefit hugely," says Matthew Stainer, NLP Specialist, SAS UK & Ireland. "Understanding this data would be a mammoth task without technology to immediately spot patterns and interpret findings. With SAS Visual Text Analytics, organisations can unlock more value from their data than ever, which in turn can power better business decisions and boost customer experiences."

Social networks empower companies with a unique opportunity to gauge the public perception of different people and ideas. This includes vital access to the consumers' feelings over specific brands and products, and the reactions they give to uncover intelligent insights. The power of AI is huge over the social media channelizing the fast, automated, accurate social analytics that extracts meaningful insights from all the chatter gone aloud. One of the ways Artificial Intelligence is being used to analyse social data chatter is sentiment analysis. Sentiment analysis leverages computational linguistics and natural language processing to decode what people say on social media channels intelligently.

Pre-trained language models such as BERT have achieved great success in a broad range of natural language processing tasks. However, BERT cannot well support E-commerce related tasks due to the lack of two levels of domain knowledge, i.e., phrase-level and product-level. On one hand, many E-commerce tasks require an accurate understanding of domain phrases, whereas such fine-grained phrase-level knowledge is not explicitly modeled by BERT's training objective. On the other hand, product-level knowledge like product associations can enhance the language modeling of E-commerce, but they are not factual knowledge thus using them indiscriminately may introduce noise. To tackle the problem, we propose a unified pre-training framework, namely, E-BERT. Specifically, to preserve phrase-level knowledge, we introduce Adaptive Hybrid Masking, which allows the model to adaptively switch from learning preliminary word knowledge to learning complex phrases, based on the fitting progress of two modes. To utilize product-level knowledge, we introduce Neighbor Product Reconstruction, which trains E-BERT to predict a product's associated neighbors with a denoising cross attention layer. Our investigation reveals promising results in four downstream tasks, i.e., review-based question answering, aspect extraction, aspect sentiment classification, and product classification.

If data is the new oil, TikTok is a gusher. It has some 100 million users in the U.S. alone. Its AI, particularly its recommendation engine, is credited with driving engagement and the app's growth to nearly 700 billion users worldwide. When President Trump signed an executive order banning the Chinese-owned app last month, he pointed out why TikTok is especially valuable. The short-form video social media application collects "vast swaths of information from its users," Trump's executive order said.

Online customer reviews have become important for managers and executives in the hospitality and catering industry who wish to obtain a comprehensive understanding of their customers' demands and expectations. We propose a Regularized Text Logistic (RTL) regression model to perform text analytics and sentiment classification on unstructured text data, which automatically identifies a set of statistically significant and operationally insightful word features, and achieves satisfactory predictive classification accuracy. We apply the RTL model to two online review datasets, Restaurant and Hotel, from TripAdvisor. Our results demonstrate satisfactory classification performance compared with alternative classifiers with a highest true positive rate of 94.9%. Moreover, RTL identifies a small set of word features, corresponding to 3% for Restaurant and 20% for Hotel, which boosts working efficiency by allowing managers to drill down into a much smaller set of important customer reviews. We also develop the consistency, sparsity and oracle property of the estimator.

Sentiment analysis is a technique through which you can analyze a piece of text to determine the sentiment behind it. It combines machine learning and natural language processing (NLP) to achieve this. Using basic Sentiment analysis, a program can understand if the sentiment behind a piece of text is positive, negative, or neutral. It is a powerful technique in Artificial intelligence that has important business applications. For example, you can use Sentiment analysis to analyze customer feedback.

What do customers want, expect, and need? Three simple questions that all corporate leaders know will ultimately determine the effectiveness of a marketing campaign, revenue of a sales drive, and success of a company. Given the cutting-edge tools on the market today, all companies have a treasure trove of valuable customer information ready to be utilized to answer these questions. Leveraging data and sentiment analysis is instrumental in grasping the challenges and seizing the opportunities of modern customer experiences. Data provides the facts, sentiment analysis the feelings.

Share this post In this post, we are implementing a real-time application of Natural Language Processing. We are going to implement the Amazon review sentiment analysis project using NLTK Library and Machine Learning in the python programming language. After reading this post, you can able to learn how amazon figures out negative, positive, and neutral response and their percentages as shown at the end of every product in Amazon. I recommend that before going in deep with the project, first go to a product in amazon and see how the reviews are classified, and how the performance measured for a product. Amazon Product - Adidas Men Shoes Table of Contents What is the Sentiment Analysis?

Social Media has let customers to communicate with their favourite brands and express their thoughts more openly than ever before. It is estimated that 80% of the world's data is unstructured, or unorganized. Huge volumes of data through emails, support tickets, chats, social media conversations are created every day which forms the supporting pillars of sentiment analysis. Being said, sentiment analysis classifiers may not be as accurate as other types of classifiers. But is it worth the effort?