In this paper, we present an open-source emotion tracker and its innovative applications. Our tracker, EmoGram, tracks emotion changes for a sequence of textual units. It is versatile in terms of the textual unit (tweets, sentences in discourse, etc.) and also what constitutes the time sequence (timestamps of tweets, discourse nature of text, etc.). We demonstrate the utility of our system through our applications: a sequence of commentaries in cricket matches, a sequence of dialogues in a play, and a sequence of tweets related to the Maggi controversy in India in 2015. That one system can be used for these applications is the merit of EmoGram.

We study the relationship between the sentiment levels of Twitter users and the evolving network structure that the users created by @-mentioning each other. We use a large dataset of tweets to which we apply three sentiment scoring algorithms, including the open source SentiStrength program. Specifically we make three contributions. Firstly we find that people who have potentially the largest communication reach (according to a dynamic centrality measure) use sentiment differently than the average user: for example they use positive sentiment more often and negative sentiment less often. Secondly we find that when we follow structurally stable Twitter communities over a period of months, their sentiment levels are also stable, and sudden changes in community sentiment from one day to the next can in most cases be traced to external events affecting the community. Thirdly, based on our findings, we create and calibrate a simple agent-based model that is capable of reproducing measures of emotive response comparable to those obtained from our empirical dataset.

Provalis Research announces today the release of a new version of its powerful text analytics software, WordStat 7.1. The software release includes a geographic information system (GIS) mapping and data editing module, allowing businesses to obtain insightful geospatial intelligence. This innovative module provides users with the ability to create a wide range of maps out of pure text data. The analysis of unstructured text data with geographic affinity poses some challenges when an organization is seeking to obtain insightful results. "The implementation of tools currently on the market is a complex process that usually requires in-depth geographic information science (GIS) knowledge," says Normand Péladeau, Provalis Research's CEO.

DigitalMR is an early stage high tech company in the space of market research and marketing. Following 4 years of focussed R&D in A.I. - financed by multiple government grants and self generated cash - we have developed a lot of unique I.P. some of which is patent pending. The main areas of our research are: text analytics - NLP, sentiment & semantic analysis, emotion detection and scoring, automated image theme and sentiment analysis. We work with blue-chip multinationals such as P&G, SABMiller, DIAGEO, Vodafone, Saxo Bank, YPO, Nielsen, TNS, and many more. We are already disrupting a 60 Billion US industry.

Synopsis: There is more to Text Mining than TDM and TF-IDF. Come explore the world of Sentiment Analysis using Advanced Text Mining techniques with cutting edge tools like Stanford's CoreNLP and analysing it's output using Python. Speaker: Aditya Shankar is a Lecturer in the Intelligent Systems practice at the Institute of Systems Science in the National University of Singapore. He started his career consulting for Microsoft in Redmond, WA, Nike in Portland, OR and T-Mobile in Seattle, WA. He then moved on to work for companies in the Healthcare domain, mostly healthcare providers in Tennessee.

Twitter was not always an advertising business, and it doesn't want to limit itself to promoted tweets. Under second-time CEO Jack Dorsey, the company has been reinvigorating its foundation as a real-time data service, and the revenue is following. Since Dorsey arrived last summer, he has been cultivating relationships with developers -- particularly those who pay for Twitter's data. In February, Dorsey listed developers as his fifth priority, behind the more obvious need to address the core service, live video, creators and safety. But interestingly, this fifth priority is the one that's already been bringing in more cash and continues to make Twitter relevant.

Avanti Recruitment is working with a successful and rapidly growing tech start-up in London to recruit a Lead Machine Learning Scientist to join their team. You will be responsible for defining and implementing the company's Machine Learning strategy and architecture for both supervised and unsupervised applications. You will be the link between the Computational Linguistic team and the Software Engineering team. You will work on sentiment analysis and real-time opinion streaming products. To be considered you will have demonstrable experience working on Machine Learning algorithms ideally for Natural Language Processing applications.

I have been trying to use ML for sentiment analysis of sentences, I have been successful with Naive Bayes and SVM but I would like to implement Neural Networks for Sentiment Analysis but couldn't find a way to convert words as input for neural networks. I know that representing word as a numerical is not efficient. How is nlpnet implemented, I tried to understand that but that flew over my head.

However, most importantly, the restaurant owner has the most scope for extracting valuable snippets of insights from customer reviews with ratings between 3-4/5. I recently had a chance to deliver a talk in a conference titled'Understanding Consumers in the Digital World', held at IIM Lucknow, Noida Campus on 16-17th November 2015. The audience mainly comprised of marketers, market research professionals and academics whose work is primarily focused on obtaining deep insights by understanding the online consumers. My talk was titled'Decoding Ratings for superior service in restaurants – Using text to understand customers'. The focus was quite simple – convince and demonstrate how to read and understand customers from their reviews, not ratings. Our product, Lunchbox, a complete restaurant management solution was showcased as well.

In an earlier blog post on Making the Business Case for Text Analytics, I had spoken of the importance of Social Media Analytics and specifically Text Analytics within the context of Social Media.for Social Media plays a critical role in today's world in understanding, measuring and influencing the real time perception of your company and/or brand. Social Media contains a wealth of information which needs to be analyzed and understood in a broader social and demographic context including, trend identification and receiving feedback from segments beyond what the traditional marketer or customer service center is accustomed to for receiving feedback. Given the sheer volume of data and the large number of users talking (posting, tweeting, etc.) about any given topic, visualization can be used very effectively in Social Media Analytics to effectively sort through the clutter and make sense of what is being said. Visualization enabled Analytics can be used to identify the trends and key influencers that may not otherwise evident.