The fashion industry is investing huge amounts of money in artificial intelligence systems that can provide what is called "sentiment analysis", which is similar to customer feedback, except that it's indirectly acquired. Typically, sentiment analytics systems can gather information from social media pages as well as through natural language processing, perhaps through listening to customer phone calls. A massive number of data points can be collated and analysed – a quantity of information that only an AI system can process. And after processing it all, conclusions can be drawn. For example, sentiment analysis can help discover whether people are speaking positively about some products or negatively about others; or it can collate and organise all reviews; or it can monitor the news media to see if the brand is being mentioned, and in what context.

An increase in the use of smartphones has laid to the use of the internet and social media platforms. The most commonly used social media platforms are Twitter, Facebook, WhatsApp and Instagram. People are sharing their personal experiences, reviews, feedbacks on the web. The information which is available on the web is unstructured and enormous. Hence, there is a huge scope of research on understanding the sentiment of the data available on the web. Sentiment Analysis (SA) can be carried out on the reviews, feedbacks, discussions available on the web. There has been extensive research carried out on SA in the English language, but data on the web also contains different other languages which should be analyzed. This paper aims to analyze, review and discuss the approaches, algorithms, challenges faced by the researchers while carrying out the SA on Indigenous languages.

The Corporate Director, Data Science is responsible for leading a team of data scientists engaged in the development and implementation of machine learning algorithms and techniques to solve business problems and optimize member experiences. This position teaches, coaches and uses a flexible, analytical approach to design, develop, and evaluate predictive models and advanced algorithms that lead to optimal value extraction from the data. Physical Requirements • Ability to travel across regions as needed. In support of the Americans with Disabilities Act, this job description lists only those responsibilities and qualifications deemed essential to the position.

But how do you turn that feedback into meaningful customer insights? In the past, companies used things like surveys to try to narrow down a general good/bad/neutral response to their recent marketing campaign or product. Still, there is so much more information in the form of unstructured data that could help companies better understand their customers. Whether they are using social media, blogs, forums, reviews, or online news commenting, customers are sharing their opinions in tons of different ways every single day. The only issue: many of these opinions are shared in nuanced ways that traditional AI hasn't been able to navigate.

But how do you turn that feedback into meaningful customer insights? In the past, companies used things like surveys to try to narrow down a general good/bad/neutral response to their recent marketing campaign or product. Still, there is so much more information in the form of unstructured data that could help companies better understand their customers. Whether they are using social media, blogs, forums, reviews, or online news commenting, customers are sharing their opinions in tons of different ways every single day. The only issue: many of these opinions are shared in nuanced ways that traditional AI hasn't been able to navigate.

Rapid advancements in predictive and prescriptive analytics have seemingly surpassed the overall utility of descriptive analytics. But as we strive to determine what will happen, and to prepare accordingly using technologies like machine learning, it is easy to forget the main value proposition of descriptive analytics which, although less celebrated, continues to endure. Descriptive analytics doesn't reveal what might happen, what should happen, or what your plan of action should be. Instead, it illustrates something much more concrete--what actually did happen and, with the proper analysis, what to do to get the most advantageous outcome out of a situation. Sentiment analysis is perhaps one of the most pervasive use cases for descriptive analytics today.

Sentiment Analysis or opinion mining is important for organisations, irrespective of industry. It helps organisations extract insights from social data and understand their customer base -- what they feel about the products and services and what else they expect from the company. Simply put, it tries to analyse the feelings of the customers hidden behind the words and it is able to do that by making use of a technology called Natural Language Processing (NLP). Today, to make the work a little easier for organisations and gain an overview of the wider public opinion behind certain topics, there are several tools available. And in this article, we are going to take a look at some of the tools one can use for sentiment analysis.

With the pervasiveness of online media data as a source of information verifying the validity of this information is becoming even more important yet quite challenging. Rumors spread a large quantity of misinformation on microblogs. In this study we address two common issues within the context of microblog social media. First we detect rumors as a type of misinformation propagation and next we go beyond detection to perform the task of rumor classification. WE explore the problem using a standard data set. We devise novel features and study their impact on the task. We experiment with various levels of preprocessing as a precursor of the classification as well as grouping of features. We achieve and f-measure of over 0.82 in RDC task in mixed rumors data set and 84 percent in a single rumor data set using a two-step classification approach.

But how do you turn that feedback into meaningful customer insights? In the past, companies used things like surveys to try to narrow down a general good/bad/neutral response to their recent marketing campaign or product. Still, there is so much more information in the form of unstructured data that could help companies better understand their customers. Whether they are using social media, blogs, forums, reviews, or online news commenting, customers are sharing their opinions in tons of different ways every single day. The only issue: many of these opinions are shared in nuanced ways that traditional AI hasn't been able to navigate.