Sentiment analysis is a technique that detects the underlying sentiment in a piece of text. It is the process of classifying text as either positive, negative, or neutral. Machine learning techniques are used to evaluate a piece of text and determine the sentiment behind it. Sentiment analysis is essential for businesses to gauge customer response. Picture this: Your company has just released a new product that is being advertised on a number of different channels.

One of these textual information is the customer comments or reviews. People usually prefer to read the reviews before buying or using a service to make the right decision. This behavior is also common before the existence of the Internet. From this amount of available data, researches attempt to handle and use these data to have a specific and useful knowledge. Sentiment analysis (SA) is the process of determining the opinion or feeling of a piece of text. Sentiment means feelings, attitudes, emotions and opinions. The applications of sentiment analysis are numerous such as politics or political science, law, e-commerce, sociology and psychology. In e-commerce, the sentiment analysis is super useful for gaining insight into customer opinions; once they understand how the customer feels after analyzing their comments or reviews, they can identify what they like and dislike and build things like recommendation systems, or enhance the product or the service.

Question Aware Open Information Extraction (Question aware Open IE) takes question and passage as inputs, outputting an answer tuple which contains a subject, a predicate, and one or more arguments. Each field of answer is a natural language word sequence and is extracted from the passage. The semi-structured answer has two advantages which are more readable and falsifiable compared to span answer. There are two approaches to solve this problem. One is an extractive method which extracts candidate answers from the passage with the Open IE model, and ranks them by matching with questions. It fully uses the passage information at the extraction step, but the extraction is independent to the question. The other one is the generative method which uses a sequence to sequence model to generate answers directly. It combines the question and passage as input at the same time, but it generates the answer from scratch, which does not use the facts that most of the answer words come from in the passage. To guide the generation by passage, we present a two-stage decoding model which contains a tagging decoder and a correction decoder. At the first stage, the tagging decoder will tag keywords from the passage. At the second stage, the correction decoder will generate answers based on tagged keywords. Our model could be trained end-to-end although it has two stages. Compared to previous generative models, we generate better answers by generating coarse to fine. We evaluate our model on WebAssertions (Yan et al., 2018) which is a Question aware Open IE dataset. Our model achieves a BLEU score of 59.32, which is better than previous generative methods.

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.