We recently announced a new version of Excel Add-in which lets you perform state-of-the-art text analysis capabilities from the comforts of your spreadsheets without writing a single line of code. The add-in has been received very well by users working across different industry verticals like Market Research, Software, Consumer Goods, Education, etc. solving a variety of use-cases. Sentiment analysis has been the most used function of our Excel add-in closely followed by Emotion detection. Many of our users use sentiment analysis in Excel to quickly and accurately analyze the responses of their open-ended surveys, online chatter around their product/service or to analyze product reviews from e-commerce sites. In this blog post, we will discuss how to use the function Sentiment Analysis in Excel Add-in to do text analytics for any type of content.

Probabilistic topic models are popular unsupervised learning methods, including probabilistic latent semantic indexing (pLSI) and latent Dirichlet allocation (LDA). By now, their training is implemented on general purpose computers (GPCs), which are flexible in programming but energy-consuming. Towards low-energy implementations, this paper investigates their training on an emerging hardware technology called the neuromorphic multi-chip systems (NMSs). NMSs are very effective for a family of algorithms called spiking neural networks (SNNs). We present three SNNs to train topic models. The first SNN is a batch algorithm combining the conventional collapsed Gibbs sampling (CGS) algorithm and an inference SNN to train LDA. The other two SNNs are online algorithms targeting at both energy- and storage-limited environments. The two online algorithms are equivalent with training LDA by using maximum-a-posterior estimation and maximizing the semi-collapsed likelihood, respectively. They use novel, tailored ordinary differential equations for stochastic optimization. We simulate the new algorithms and show that they are comparable with the GPC algorithms, while being suitable for NMS implementation. We also propose an extension to train pLSI and a method to prune the network to obey the limited fan-in of some NMSs.

Dialog state tracking is to accurately infer a compact representation of the dialog status up to the current turn, it needs to summarize all the dialog history information and user's goals. In a successful spoken dialog system, dialog state tracker is one of the most important components of the pipelines. Yet until recently, there are no general, flexible, accurate and truly end to end dialog state tracking models. In this paper, we propose a novel model named state tracking networks that can perform dialog state tracking in a natural efficient and elegant way. It uses an explicit gate to model the state updating mechanism and can be trained end to end in a deterministic manner using standard backpropagation techniques or stochastically by reinforcement learning. Our model can both deal with ASR and text input without any modification. We perform experiments on the Second Dialog State Tracking Challenge dataset(DSTC2) and get performance matching the state-of-the-art models. Furthermore, the qualitative analysis reveals that the gating mechanism learned by our model agree well with intuition.

The ability to identify sentiment in text, referred to as sentiment analysis, is one which is natural to adult humans. This task is, however, not one which a computer can perform by default. Identifying sentiments in an automated, algorithmic manner will be a useful capability for business and research in their search to understand what consumers think about their products or services and to understand human sociology. Here we propose two new Genetic Algorithms (GAs) for the task of automated text sentiment analysis. The GAs learn whether words occurring in a text corpus are either sentiment or amplifier words, and their corresponding magnitude. Sentiment words, such as 'horrible', add linearly to the final sentiment. Amplifier words in contrast, which are typically adjectives/adverbs like 'very', multiply the sentiment of the following word. This increases, decreases or negates the sentiment of the following word. The sentiment of the full text is then the sum of these terms. This approach grows both a sentiment and amplifier dictionary which can be reused for other purposes and fed into other machine learning algorithms. We report the results of multiple experiments conducted on large Amazon data sets. The results reveal that our proposed approach was able to outperform several public and/or commercial sentiment analysis algorithms.

Code-mixed data is an important challenge of natural language processing because its characteristics completely vary from the traditional structures of standard languages. In this paper, we propose a novel approach called Sentiment Analysis of Code-Mixed Text (SACMT) to classify sentences into their corresponding sentiment - positive, negative or neutral, using contrastive learning. We utilize the shared parameters of siamese networks to map the sentences of code-mixed and standard languages to a common sentiment space. Also, we introduce a basic clustering based preprocessing method to capture variations of code-mixed transliterated words. Our experiments reveal that SACMT outperforms the state-of-the-art approaches in sentiment analysis for code-mixed text by 7.6% in accuracy and 10.1% in F-score.

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.