Structural correspondence learning (SCL) is an effective method for cross-lingual sentiment classification. This approach uses unlabeled documents along with a word translation oracle to automatically induce task specific, cross-lingual correspondences. It transfers knowledge through identifying important features, i.e., pivot features. For simplicity, however, it assumes that the word translation oracle maps each pivot feature in source language to exactly only one word in target language. This one-to-one mapping between words in different languages is too strict. Also the context is not considered at all. In this paper, we propose a cross-lingual SCL based on distributed representation of words; it can learn meaningful one-to-many mappings for pivot words using large amounts of monolingual data and a small dictionary. We conduct experiments on NLP\&CC 2013 cross-lingual sentiment analysis dataset, employing English as source language, and Chinese as target language. Our method does not rely on the parallel corpora and the experimental results show that our approach is more competitive than the state-of-the-art methods in cross-lingual sentiment classification.

Keras is a great library for hands-on on neural networks, and it has a ton of great examples that makes it very easy to create ANNs & DNNs. So easy in fact, that you could even build one without knowing what's going on. I used the CNN model from this Keras blog post to create a simple sentiment analysis model. But to fully understand what I had just done, I had to dig a little deeper. The basic model outlined in the post is using pre-trained word embeddings of the text to train a CNN for sentiment analysis. I have shown it below, with a few minor changes to padding sizes (border_mode'same'), so that the convolution output size stays the same as its input (for simplicity).

The world of big data analytics is incredibly diverse, and people are coming up with new analytic tools and techniques every day. But one particularly productive combination that should not be overlooked involves the use of text analytics and machine learning. Tom Sabo, principal solutions architect at analytics giant SAS, says the one-two punch of predictive modeling on structured data, and text mining with unstructured data, can deliver insights that are more than the sum of their analytic parts. "They really run side by side," Sabo tells Datanami. "Let's say somebody has predictive models in place against whether customer will churn or to maximize profit, for instance. If they have text, like notes, in the rest of that structured data…we can incorporate that additional free form information for actionable insight."

For example, if you were to train solely on content without any view into how the system is making its decisions, that system might learn that the phrase "Greek bank" is negative, due to the deluge of negative stories associated with Greek banks over the years, even though the phrase is not inherently negative. This is a common problem with systems that attempt to analyze sentiment with a single model and will skew results over time. The Lexalytics HSDTrainer can consume any text corpus that has been appropriately marked up for sentiment, and then return a list of phrases and suggested scores for that text corpus, allowing analysts to both rapidly and transparently train sentiment. Emoji Analytics -- With Salience 6.2, social marketers can now analyze the meaning and sentiment of content that includes the latest emojis released in Unicode 9.0. For example, if a food manufacturer releases a new product that elicits social media posts with the new "nauseated face" emoji, Lexalytics can score the content as negative and alert the customer. Conversely, those same marketers can search for anything that mentions "nausea," and that emoji will return a hit.

Online “notice and choice” is an essential concept in the US FTC’s Fair Information Practice Principles. Privacy laws based on these principles include requirements for providing notice about data practices and allowing individuals to exercise control over those practices. Internet users need control over privacy, but their options are hidden in long privacy policies which are cumbersome to read and understand. In this paper, we describe several approaches to automatically extract choice instances from privacy policy documents using natural language processing and machine learning techniques. We define a choice instance as a statement in a privacy policy that indicates the user has discretion over the collection, use, sharing, or retention of their data. We describe supervised machine learning approaches for automatically extracting instances containing opt-out hyperlinks and evaluate the proposed methods using the OPP-115 Corpus, a dataset of annotated privacy policies. Extracting information about privacy choices and controls enables the development of concise and usable interfaces to help Internet users better understand the choices offered by online services. The focus of this paper, however, is to describe such methods to automatically extract useful opt-out hyperlinks from privacy policies.

LiveEngage from LivePerson is a chat capability for ecommerce and web sites that gives Episerver customers a way to communicate directly with website visitors through a chat, increasing sales and customer satisfaction and loyalty. With a full back end agent interface that provides usage statistics and sentiment analysis, you get excellent insights into the needs of your customers.

Such sentiment analysis, however, comes with a heavy workload and also requires mathematical models. "There are three ways to make improved predictions – a better model, better data, and more data," says Jeremy Perlman, VP Europe for Trifacta, which helps RBS, Santander and PepsiCo analyse data. "The problem is that data created on social media and the web is expanding at a ridiculous rate, so machine learning will be critical to making better predictions at massive scale." Since computing power is increasingly exponential with the birth of super-computing in the cloud, the need to analyse more and more data shouldn't be a major hurdle. "Computational devices can very effectively, with high precision and rapidly, gather millions of tweets, posts or similar and run sentiment analysis – to understand likes and dislikes," says Jepson.

This article is designed to help you quickly set up a simple Azure Stream Analytics job, with Azure Machine Learning integration. We will use a sentiment analytics Machine Learning model from the Cortana Intelligence Gallery to analyze streaming text data, and determine the sentiment score in real time. The information in this article can help you understand scenarios such as real-time sentiment analytics on streaming Twitter data, analyze records of customer chats with support staff, and evaluate comments on forums, blogs, and videos, in addition to many other real-time, predictive scoring scenarios. This article offers a sample CSV file with text as input in Azure Blob storage, shown in the following image. The job applies the sentiment analytics model as a user-defined function (UDF) on the sample text data from the blob store.

As part of my Technology and Innovation MBA program at Ted Rogers School of Management, I took a data and knowledge management course which teaches students the principles and practices of knowledge management. The second part of the course delves on tools used in data management and analytics. Although the theoretical part of the course was a bit dry, the hands-on portion was very interesting and exposed students to several different tools to capture, clean and analyze data. One of the tasks given to students was to capture and analyze twitter data. Although students had access to Netlytics, which is a neat cloud-based text and social network analysis tool that also collects Twitter data, students were encouraged to find other ways to collect Twitter data.

One of the most difficult challenges reporting and analytics face in public relations measurement is sentiment analysis. Machines attempt textual analysis of sentiment all the time; more often than not, it goes horribly wrong. How does it go wrong? Machines are incapable of understanding context. Machines are typically programmed to look for certain keywords as proxies for sentiment.