Discourse & Dialogue

When considering what information is important for a certain decision procedure (say, a classification task), there's an interesting gap between what's theoretically --that is, actually-- important on the one hand and what gives good results in practice as input to machine learning (ML) algorithms, on the other. On the other hand, embedding syntactic structures in a vector space while making the distance relation meaningful is not quite as easy. Funnily enough, when I tried the two Dancing Monkeys in a Tuxedo sentences with Stanford's recursive sentiment analysis tool, it classified both sentences as negative. What you can do in this case is to restructure your input vector so that instead of having a unique, separate feature for the sentiment of the review, you use feature combinations (also called feature crosses) so that all word frequency features include information about the sentiment.

No - you can't call it a good model. In the domain you are talking about, we are more interested in catching a true churner than catching a true non-churner. Now from your data you can find - if you use the 0.8 as the cutoff - what %of true churners you correctly predict (true ve) and what % of true non-churners you wrongly label as churners (false ve). ROC tells you, what should be your cutoff and to get there how much false ve you need to tolerate.

Mining such data to determine how people feel about your product, brand, or service, is called Sentiment Analysis. When applied to social media channels, it can be used to identify spikes in sentiment, thereby allowing you to identify potential product advocates or social media influencers. Companies such as Microsoft, IBM and smaller emerging companies offer REST APIs that integrate easily with your existing software applications. For example, using the following publicly available Sentiment Analysis REST API from a small start-up called Social Opinion, we pass in the text, "this phone is awesome", to the following URL: In the response, we can see the text has been identified as expressing positive emotion, with a 64% probability of that being true.

In this study, we analysed more than 400 thousand reviews of unlocked mobile phones sold on Amazon.com to find insights with respect to reviews, ratings, price and their relationships. The plot between average review length and rating will help us find out if the products with detailed reviews attract better rating. We segregated the reviews according to their ratings – positive reviews (4 or 5 star) and negative reviews (1 or 2 star). Amazon's product review platform shows that most of the reviewers have given 4-star and 3-star ratings to unlocked mobile phones.

Mining such data to determine how people feel about your product, brand, or service, is called Sentiment Analysis. When applied to social media channels, it can be used to identify spikes in sentiment, thereby allowing you to identify potential product advocates or social media influencers. Companies such as Microsoft, IBM and smaller emerging companies offer REST APIs that integrate easily with your existing software applications. For example, using the following publicly available Sentiment Analysis REST API from a small start-up called Social Opinion, we pass in the text, "this phone is awesome", to the following URL: In the response, we can see the text has been identified as expressing positive emotion, with a 64% probability of that being true.

"American consumers continued to disagree with themselves in June as consumer confidence has remained high but has still not translated into much higher consumption," Eugenio Alemán, a senior economist at Wells Fargo Securities, wrote in a research note Friday. "Although personal consumption expenditures are expected to bounce back during the second quarter of the year, the bounce back may not be as strong as what we were expecting if these numbers remain as they were originally published, which is a big if."

For our case we only consider Text feature of the Tweet as we are interested on the review of the movie. We can also use the other features such as Latitude/Longitude, replied to, etc. do other analysis on the tweeted data.

So having quick access to information is critical for making rational business decisions. BI is an umbrella term that refers to a variety of software applications used to analyze data and support a wide spectrum of business decisions, ranging from operational to strategic. Another promising area of NLP application in BI is sentiment analysis -- the use of natural language processing techniques to extract subjective information from a piece of text, also known as Opinion Mining. In our next article that will come soon we will review and compare the TOP Natural Language Processing APIs -- make sure not to miss it!

Conference Topics Topics at this conference include, but are not limited to: Business Analytics - Methods: Dimensionality Reduction, Feature Extraction, and Feature Selection Supervised, Semi-Supervised, and Unsupervised Methods Statistical Learning Theory Online Learning, Data Stream Mining, and Dynamic Data Mining Graph Mining and Semi-Structured Data patial and Temporal Data Mining Deep Learning and Neural Network Research Large Scale Data Mining Uncertainty Modeling in Data Mining Business Analytics - Applications: Credit Scoring and Financial Modeling Forecasting Fraud Detection Web Intelligence and Information Retrieval Marketing, Business Intelligence, and e-Commerce Decision Analysis and Decision Support Systems Social Network Analysis Privacy-preserving Data Mining and Privacy-related Issue Text Mining, Sentiment Analysis, and Opinion Mining Important Dates July 31, 2017: Deadline for submission of extended abstracts August 15, 2017: Accept/reject decision November 15, 2017: Deadline for early registration January 17-19, 2018: BAFI 2018 *Only one contributed abstract is accepted from the same presenting author. Submission Guidelines Authors are requested to submit a 600 word abstract in English using the platform available at the EasyChair system. Please do not attach any additional files at this time.

Artificial Intelligence, Machine Learning and Sentiment Analysis are changing the way in which numerous client services are offered. This trend is now being taken on board by multiple innovators: academia, start-ups, technology companies and financial market participants. AI and Machine Learning have emerged as a central aspect of analytics which is applied to multiple domains. AI and Machine Learning, Pattern classifiers and natural language processing (NLP) underpin Sentiment Analysis (SA); SA is a technology that makes rapid assessment of the sentiments expressed in news releases as well as other media sources such as Twitter and blogs.