In this blog post, the third one of our Topic Models series, we are showcasing how you can use BigML Topic Models to improve your model performance. We are using movie reviews extracted from the IMBD database to predict if a given review has a positive or a negative sentiment. Notice that in this post we will not dive into all the configuration options that BigML offers for Topic Models, for that we recommend that you read our previous post. The dataset contains 50,000 highly polarized movie reviews labeled with their sentiment class: positive or negative. This dataset was built by Stanford researchers for their paper from 2011, which achieves an accuracy of 88.89%.

As part of my ramp up on Google APIs I wanted to create a project that would allow me some practical exercise in a context of a real application. All GCP services used in this example can be run under the GCP Free Tier plan. More more information see https://cloud.google.com/free/ The Go code, docs, and setup scripts are located in my GitHub repo.

Machine learning is used for generating information and knowledge via a technical system, e.g., a software. The system learns patterns or structures using previous examples and is subsequently able to independently evaluate and classify information and data. Sentiment analysis, content monitoring, technology categorization, predictive coding, clustering, alerting, and documents search. Machine learning has already become very important in the context of big data since it enables processing large amounts of data quickly and easily.

For a more technical explanation, this and this article can be read. Here you can find a good explanation as well as a list of the mostly used Kernel functions.

Using code KDNUGGETS to save - early reg by May 31.

It is difficult to train a personalized task-oriented dialogue system because the data collected from each individual is often insufficient. Personalized dialogue systems trained on a small dataset can overfit and make it difficult to adapt to different user needs. One way to solve this problem is to consider a collection of multiple users' data as a source domain and an individual user's data as a target domain, and to perform a transfer learning from the source to the target domain. By following this idea, we propose "PETAL"(PErsonalized Task-oriented diALogue), a transfer-learning framework based on POMDP to learn a personalized dialogue system. The system first learns common dialogue knowledge from the source domain and then adapts this knowledge to the target user. This framework can avoid the negative transfer problem by considering differences between source and target users. The policy in the personalized POMDP can learn to choose different actions appropriately for different users. Experimental results on a real-world coffee-shopping data and simulation data show that our personalized dialogue system can choose different optimal actions for different users, and thus effectively improve the dialogue quality under the personalized setting.

Organizations have generated unprecedented amounts of employee feedback through weekly or monthly pulse surveys, annual engagement surveys, and internal social networks and collaboration platforms. But many still struggle with how to efficiently comb through that mountain of information to identify actionable insights leaders can use to improve employee engagement and retention. Some companies are now turning to artificial intelligence (AI) tools to conduct sentiment analysis on employee feedback, gauge how employees feel and address their concerns. While text analysis of survey responses isn't new, the emergence of smarter algorithms enables faster and more precise search and categorization of unstructured data, such as open-ended comments, said Alan Lepofsky, vice president and principal analyst with Constellation Research, a technology research firm in Silicon Valley. Lepofsky, author of the recent report Why Artificial Intelligence Will Power the Future of Work, said vendors have made advances in sentiment analysis technology.

Topic models for text corpora comprise a popular family of methods that have inspired many extensions to encode properties such as sparsity, interactions with covariates, and the gradual evolution of topics. In this paper, we combine certain motivating ideas behind variations on topic models with modern techniques for variational inference to produce a flexible framework for topic modeling that allows for rapid exploration of different models. We first discuss how our framework relates to existing models, and then demonstrate that it achieves strong performance, with the introduction of sparsity controlling the trade off between perplexity and topic coherence. We have released our code and preprocessing scripts to support easy future comparisons and exploration.

With Firebase and Cloud Functions, you can easily add machine learning to your app to do translation, sentiment analysis, speech recognition, and computer vision. See all the talks from Google I/O '17 here: https://goo.gl/D0D4VE

Text representation in Chinese sentiment analysis is usually working at word or character level. In this paper, we prove that radical-level processing could greatly improve sentiment classification performance. In particular, we propose two types of Chinese radical-based hierarchical embeddings. The embeddings incorporate not only semantics at radical and character level, but also sentiment information. In the evaluation of our embeddings, we conduct Chinese sentiment analysis at sentence level on four different datasets. Experimental results validate our assumption that radical-level semantics and sentiments can contribute to sentence-level sentiment classification and demonstrate the superiority of our embeddings over classic textual features and popular word and character embeddings.