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.

About this course: This course covers a wide range of tasks in Natural Language Processing from basic to advanced: sentiment analysis, summarization, dialogue state tracking, to name a few. Upon completing, you will be able to recognize NLP tasks in your day-to-day work, propose approaches, and judge what techniques are likely to work well. The final project is devoted to one of the most hot topics in today's NLP. You will build your own conversational chat-bot that will assist with search on StackOverflow website. The project will be based on practical assignments of the course, that will give you hands-on experience with such tasks as text classification, named entities recognition, and duplicates detection.

The belief that humans will be able to interact with computers in conversational speech has long been a favorite subject in science fiction, reflecting the persistent belief that spoken dialogue would be the most natural and powerful user interface to computers. With recent improvements in computer technology and in speech and language processing, such systems are starting to appear feasible. There are significant technical problems that still need to be solved before speech-driven interfaces become truly conversational. This article describes the results of a 10-year effort building robust spoken dialogue systems at the University of Rochester. For example, consider building a telephony system that answers queries about your mortgage.

This special issue of AI Magazine on dialogue with robots brings together a collection of articles on situated dialogue. The contributing authors have been working in interrelated fields of human-robot interaction, dialogue systems, virtual agents, and other related areas and address core concepts in spoken dialogue with embodied robots or agents. Several of the contributors participated in the AAAI Fall Symposium on Dialog with Robots, held in November 2010, and several articles in this issue are extensions of work presented there. The articles in this collection address diverse aspects of dialogue with robots, but are unified in addressing opportunities with spoken language interaction, physical embodiment, and enriched representations of context. Research on computational models and mechanisms for supporting spoken dialogue dates back to the earliest days of AI research, including Alan Turing's reflection about how machine intelligence could be evaluated.

The Dialogue on Dialogues workshop was organized as a satellite event at the Interspeech 2006 conference in Pittsburgh, Pennsylvania, and it was held on September 17, 2006, immediately before the main conference. It was planned and coordinated by Michael McTear (University of Ulster, UK), Kristiina Jokinen (University of Helsinki, Finland), and James A. Larson (Portland State University, USA). The one-day workshop involved more than 40 participants from Europe, the United States, Australia, and Japan. One of the motivations for furthering the systems' interaction capabilities is to improve the AI Magazine Volume 28 Number 2 (2007) ( AAAI) However, relatively little work has so far been devoted to defining the criteria according to which we could evaluate such systems in terms of increased naturalness and usability. It is often felt that statistical speech-based research is not fully appreciated in the dialogue community, while dialogue modeling in the speech community seems too simple in terms of the advanced architectures and functionalities under investigation in the dialogue community.

Many common mistakes can be avoided when testing sentiment data for predictive properties. The term "prediction" is not a legal definition. In assessing the predictive qualities of sentiment data there are no rules for what counts as a signal to be tested for predictive properties with regard to financial assets. However, the method you chose ultimately defines what you mean with the term "prediction". To illustrate the point: Using a more prudent definition of the term, the accuracy in the world's most famous prediction study could have been as low as 47% (7 out of 15) instead of 87% (13 out of 15%).