Marketing analytics is a diverse field, with both academic researchers and practitioners coming from a range of backgrounds including marketing, operations research, statistics, and computer science. This paper provides an integrative review at the boundary of these three areas. The topics of visualization, segmentation, and class prediction are featured. Links between the disciplines are emphasized. For each of these topics, a historical overview is given, starting with initial work in the 1960s and carrying through to the present day. Recent innovations for modern large and complex "big data" sets are described. Practical implementation advice is given, along with a directory of open source R routines for implementing marketing analytics techniques.

Machine learning and artificial intelligence are so difficult to understand, only a few very smart computer scientists know how to build them. But the designers of a new tool have a big ambition: to create the Javascript for AI. The tool, called Cortex, uses a graphical user interface to make it so that building an AI model doesn't require a PhD. The honeycomb-like interface, designed by Mark Rolston of Argodesign, enables developers–and even designers–to use premade AI "skills," as Rolston describes them, that can do things like sentiment analysis or natural language processing. They can then drag and drop these skills into an interface that shows the progression of the model.

Generic generation and manipulation of text is challenging and has limited success compared to recent deep generative modeling in visual domain. This paper aims at generating plausible natural language sentences, whose attributes are dynamically controlled by learning disentangled latent representations with designated semantics. We propose a new neural generative model which combines variational auto-encoders and holistic attribute discriminators for effective imposition of semantic structures. With differentiable approximation to discrete text samples, explicit constraints on independent attribute controls, and efficient collaborative learning of generator and discriminators, our model learns highly interpretable representations from even only word annotations, and produces realistic sentences with desired attributes. Quantitative evaluation validates the accuracy of sentence and attribute generation.

So, Naive Bayes gives very bad result. It can just predict 11% of bad comments. SGDClassifier predicted 47% of bad comments correctly which is a considerable improvement over the Naive Bayes. Logistic Regression though has regression in its surname but its a classifier and it shows good improvement over SGDClassifier. SVC comes out as winner with 66 % correct prediction for sentiment analysis.

This is the 6th part of my ongoing Twitter sentiment analysis project. You can find the previous posts from the below links. Before we jump into doc2vec, it will be better to mention word2vec first. "Word2vec is a group of related models that are used to produce word embeddings. These models are shallow, two-layer neural networks that are trained to reconstruct linguistic contexts of words."

In this project, I will demonstate how to perform sentiment analysis on tweets using various C# libraries. All of the code below will be placed in the Program class. Thanks to the Tweetinvi library, the authentication with the Twitter API is a breeze. Assuming that an application has been registered at http://apps.twitter.com, This type of global authentication makes it easy to perform authenticated calls throughout the entire application.

Sentiment analysis is contextual mining of text which identifies and extracts subjective information in source material, and helping a business to understand the social sentiment of the brand, product or service while monitoring online conversations. However, analysis of social media streams is usually restricted to just basic sentiment analysis and count based metrics. This is akin to just scratching the surface and missing out on those high value insights that are waiting to be discovered. So what should a brand do to capture that low hanging fruit? With the recent advances in deep learning, the ability of algorithms to analyse text has improved considerably.

Staying on top of recent work is an important part of being a good researcher, but this can be quite difficult. Thousands of new papers are published every year at the main ML and NLP conferences, not to mention all the specialised workshops and everything that shows up on ArXiv. Going through all of them, even just to find the papers that you want to read in more depth, can be very time-consuming. In this post, I have summarised 50 papers. After going through a paper, if I had the chance, I would write down a few notes and summarise the work in a couple of sentences. These are not meant as reviews – I'm not commenting on whether I think the paper is good or not. But I do try to present the crux of the paper as bluntly as possible, without unnecessary sales tactics. Hopefully this can give you the general idea of 50 papers, in roughly 20 minutes of reading time. The papers are not selected or ordered based on any criteria. It is not a list of the best papers I have read, more like a random sample.

Somewhere, someone is tweeting "[This airline] sucks the big one!" In the past, they would have been ignored. These days many airlines respond with sympathy ("We're so sorry you're having a rough trip -- please DM us, so we can resolve it") or send an invitation to call an 800-number (where you can wait on hold forever). A tool called sentiment analysis, or the mathematical categorization of statements' negative or positive connotations, gives companies powerful ways to analyze aggregate language data across all sorts of communications, not only tweets. Here are five of the most valuable sentiment sources to tap.

Topic modeling enables exploration and compact representation of a corpus. The CaringBridge (CB) dataset is a massive collection of journals written by patients and caregivers during a health crisis. Topic modeling on the CB dataset, however, is challenging due to the asynchronous nature of multiple authors writing about their health journeys. To overcome this challenge we introduce the Dynamic Author-Persona topic model (DAP), a probabilistic graphical model designed for temporal corpora with multiple authors. The novelty of the DAP model lies in its representation of authors by a persona --- where personas capture the propensity to write about certain topics over time. Further, we present a regularized variational inference algorithm, which we use to encourage the DAP model's personas to be distinct. Our results show significant improvements over competing topic models --- particularly after regularization, and highlight the DAP model's unique ability to capture common journeys shared by different authors.