Beyond enhancing robotics and personal devices, emotion AI can be applied in customer experience initiatives, such as VoC programs. A fleet of vendors already offer sentiment analysis by mining billions of data points on social media platforms and user forums.

Top Stories, Mar 19-25: 5 Things You Need to Know about Sentiment Analysis and Classification; Top 12 Essential Command Line Tools for Data Scientists Top KDnuggets tweets, Mar 14-20: Introduction to Markov Chains "What are Markov chains, when to use them, and how they work"

In the last years, Sentiment Analysis has become a hot-trend topic of scientific and market research in the field of Natural Language Processing (NLP) and Machine Learning. Below, you can find 5 useful things you need to know about Sentiment Analysis that are connected to Social Media, Datasets, Machine Learning, Visualizations, and Evaluation Methods applied by researchers and market experts. Sentiment Analysis examines the problem of studying texts, like posts and reviews, uploaded by users on microblogging platforms, forums, and electronic businesses, regarding the opinions they have about a product, service, event, person or idea. The most common use of Sentiment Analysis is this of classifying a text to a class. Depending on the dataset and the reason, Sentiment Classification can be binary (positive or negative) or multi-class (3 or more classes) problem.

Qemotion asks users to submit a text using API and the algorithm will detect the main emotion of the speech and will define the corresponding emotion in terms of temperature (literally temperature).

Social networking sites such as Twitter have provided a great opportunity for organizations such as public libraries to disseminate information for public relations purposes. However, there is a need to analyze vast amounts of social media data. This study presents a computational approach to explore the content of tweets posted by nine public libraries in the northeastern United States of America. In December 2017, this study extracted more than 19,000 tweets from the Twitter accounts of seven state libraries and two urban public libraries. Computational methods were applied to collect the tweets and discover meaningful themes. This paper shows how the libraries have used Twitter to represent their services and provides a starting point for different organizations to evaluate the themes of their public tweets.

Over the last four months, I attended an immersive data science program at Galvanize in San Francisco. As a graduation requirement, the last three weeks of the program are reserved for a student-selected project that puts to use the skills learned throughout the course. The project that I chose to tackle utilized natural language processing in tandem with sentiment analysis to parse and classify news articles. With the controversy surrounding our nation's media and the concept of "fake news" floated around every corner, I decided to take a pragmatic approach to address bias in the media. My resulting model identified three topics within an article and classified the sentiments towards each topic.

Motivated by Supervised Opinion Analysis, we propose a novel framework devoted to Structured Output Learning with Abstention (SOLA). The structure prediction model is able to abstain from predicting some labels in the structured output at a cost chosen by the user in a flexible way. For that purpose, we decompose the problem into the learning of a pair of predictors, one devoted to structured abstention and the other, to structured output prediction. To compare fully labeled training data with predictions potentially containing abstentions, we define a wide class of asymmetric abstention-aware losses. Learning is achieved by surrogate regression in an appropriate feature space while prediction with abstention is performed by solving a new pre-image problem. Thus, SOLA extends recent ideas about Structured Output Prediction via surrogate problems and calibration theory and enjoys statistical guarantees on the resulting excess risk. Instantiated on a hierarchical abstention-aware loss, SOLA is shown to be relevant for fine-grained opinion mining and gives state-of-the-art results on this task. Moreover, the abstention-aware representations can be used to competitively predict user-review ratings based on a sentence-level opinion predictor.

Dynamic topic models (DTMs) model the evolution of prevalent themes in literature, online media, and other forms of text over time. DTMs assume that word co-occurrence statistics change continuously and therefore impose continuous stochastic process priors on their model parameters. These dynamical priors make inference much harder than in regular topic models, and also limit scalability. In this paper, we present several new results around DTMs. First, we extend the class of tractable priors from Wiener processes to the generic class of Gaussian processes (GPs). This allows us to explore topics that develop smoothly over time, that have a long-term memory or are temporally concentrated (for event detection). Second, we show how to perform scalable approximate inference in these models based on ideas around stochastic variational inference and sparse Gaussian processes. This way we can train a rich family of DTMs to massive data. Our experiments on several large-scale datasets show that our generalized model allows us to find interesting patterns that were not accessible by previous approaches.

In this paper we study the frequentist convergence rate for the Latent Dirichlet Allocation (Blei et al., 2003) topic models. We show that the maximum likelihood estimator converges to one of the finitely many equivalent parameters in Wasserstein's distance metric at a rate of $n^{-1/4}$ without assuming separability or non-degeneracy of the underlying topics and/or the existence of more than three words per document, thus generalizing the previous works of Anandkumar et al. (2012, 2014) from an information-theoretical perspective. We also show that the $n^{-1/4}$ convergence rate is optimal in the worst case.

Advanced topic extraction using AI & machine learning, natural language processing, and regex classifiers to identify topics across all data sources - enabling organizations to diagnose not only what is happening in customer interactions, but why it's happening. Sentiment analysis and entity recognition using proprietary and open source algorithms to understand the overall sentiment and label data by types such as person, organization, location, events, and products. Pre-configured industry dashboards and classifier libraries that address the most common uses cases for sales, churn and compliance to jumpstart time-to-value. Data connectors to a variety of data sources and destinations, send classified data to an internal visualization tool or build powerful apps using the ListenLogic API. On premise or cloud deployment leveraging proprietary data redaction that removes personally identifiable information for an added level of security.