Some enterprises use Clarabridge to mine customer data, manage customer experience, and see sentiment analysis. While Clarabridge provides an intelligence platform, Signals is a more powerful solution platform in unifying customer voice. While sentiment analysis is a key function of Signals, its deep machine learning capability allows you to do something more organic. It enables you to listen to your customer data from the ground up and identify trends and patterns as they emerge. Signals results are displayed directly in front of the user.

IBM surveyed more than 600 decision-makers about their cognitive initiatives and 62 percent of respondents stated that the results of their cognitive implementations exceed expectations*. Cognitive services, like those offered byIBM Watson, can help you find out how your customers feel and help you predict what they might do. With Watson, IBM is pioneering the development of models that can tell you about different and often hidden, aspects of an individual. These insights can then be used by an organization to deepen relationships, shape initiatives and drive innovation. REST APIs, like Watson Personality Insights and Watson Emotion Analysis, allow organizations to learn about an individual's: Organizations can now train apps to quickly analyze and interpret large volumes of unstructured sensory data.

This repository contains the TSFRESH python package. "Time Series Feature extraction based on scalable hypothesis tests". The package contains many feature extraction methods and a robust feature selection algorithm. Data Scientists often spend most of their time either cleaning data or building features. While we cannot change the first thing, the second can be automated.

Natural language processing is an ever-growing interest area in the analytics application spectrum and is relevant to HR. In fact, it can revolutionize the quality of insights. In this article, we will explain you how. Did you know that text analysis has been the most prevalent productivity tool over the past 3 decades or so for HR? It is very familiar to HR. HR has been using Boolean keyword searches for identifying good resumes/ job applications for a long time already.

Over this course you will build multiple practical systems using natural language processing (NLP), the branch of machine learning and data science that deals with text and speech. You'll start with a background on NLP before diving in, building a spam detector and a model for sentiment analysis in Python. Learning how to build these practical tools will give you an excellent window into the mechanisms that drive machine learning. Build a spam detector & sentiment analysis model that may be used to predict the stock market Learn practical tools & techniques like the natural language toolkit library & latent semantic analysis Create an article spinner from scratch that can be used as an SEO tool Think this is cool? Check out the other bundles in this series, The Deep Learning and Artificial Intelligence Introductory Bundle, and The Advanced Guide to Deep Learning and Artificial Intelligence.

Cross-domain sentiment classification aims to tag sentiments for a target domain by labeled data from a source domain. Due to the difference between domains, the accuracy of a trained classifier may be very low. In this paper, we propose a boosting-based learning framework named TR-TrAdaBoost for cross-domain sentiment classification. We firstly explore the topic distribution of documents, and then combine it with the unigram TrAdaBoost. The topic distribution captures the domain information of documents, which is valuable for cross-domain sentiment classification. Experimental results indicate that TR-TrAdaBoost represents documents well and boost the performance and robustness of TrAdaBoost.

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.

In this paper, we focus on the task of extracting named entities together with their associated sentiment information in a joint manner. Our key observation in such an entity-level sentiment analysis (a.k.a. targeted sentiment analysis) task is that there exists a sentiment scope within which each named entity is embedded, which largely decides the sentiment information associated with the entity. However, such sentiment scopes are typically not explicitly annotated in the data, and their lengths can be unbounded. Motivated by this, unlike traditional approaches that cast this problem as a simple sequence labeling task, we propose a novel approach that can explicitly model the latent sentiment scopes. Our experiments on the standard datasets demonstrate that our approach is able to achieve better results compared to existing approaches based on conventional conditional random fields (CRFs) and a more recent work based on neural networks.

Huge volumes of opinion-rich data is user-generated in social media at an unprecedented rate, easing the analysis of individual and public sentiments. Sentiment analysis has shown to be useful in probing and understanding emotions, expressions and attitudes in the text. However, the distinct characteristics of social media data present challenges to traditional sentiment analysis. First, social media data is often noisy, incomplete and fast-evolved which necessitates the design of a sophisticated learning model. Second, sentiment labels are hard to collect which further exacerbates the problem by not being able to discriminate sentiment polarities. Meanwhile, opportunities are also unequivocally presented. Social media contains rich sources of sentiment signals in textual terms and user interactions, which could be helpful in sentiment analysis. While there are some attempts to leverage implicit sentiment signals in positive user interactions, little attention is paid on signed social networks with both positive and negative links. The availability of signed social networks motivates us to investigate if negative links also contain useful sentiment signals. In this paper, we study a novel problem of unsupervised sentiment analysis with signed social networks. In particular, we incorporate explicit sentiment signals in textual terms and implicit sentiment signals from signed social networks into a coherent model SignedSenti for unsupervised sentiment analysis. Empirical experiments on two real-world datasets corroborate its effectiveness.

The task of aspect and opinion terms co-extraction aims to explicitly extract aspect terms describing features of an entity and opinion terms expressing emotions from user-generated texts. To achieve this task, one effective approach is to exploit relations between aspect terms and opinion terms by parsing syntactic structure for each sentence. However, this approach requires expensive effort for parsing and highly depends on the quality of the parsing results. In this paper, we offer a novel deep learning model, named coupled multi-layer attentions. The proposed model provides an end-to-end solution and does not require any parsers or other linguistic resources for preprocessing. Specifically, the proposed model is a multi-layer attention network, where each layer consists of a couple of attentions with tensor operators. One attention is for extracting aspect terms, while the other is for extracting opinion terms. They are learned interactively to dually propagate information between aspect terms and opinion terms. Through multiple layers, the model can further exploit indirect relations between terms for more precise information extraction. Experimental results on three benchmark datasets in SemEval Challenge 2014 and 2015 show that our model achieves state-of-the-art performances compared with several baselines.