The 1st International Workshop on Natural Language Processing for Informal Text (NLPIT 2015)

The 1st International Workshop on  Natural Language Processing for Informal Text  (NLPIT 2015)
June 23, 2015, Rotterdam, The Netherlands


We invite submissions on topics that include, but are not limited to, the following core NLP approaches for informal UGC: language identification, classification, clustering, filtering, summarization, tokenization, segmentation, morphological analysis, POS tagging, parsing, named entity extraction, named entity disambiguation, relation/fact extraction, semantic annotation, sentiment analysis, language normalization, informality modeling and measuring, language generation, handling uncertainties, machine translation, ontology construction, dictionary construction, etc.


Authors are invited to submit original work not submitted to another conference or workshop. Workshop submissions could be a full paper or short paper. Paper length should not exceed 12 pages for full papers and 6 pages for short papers. All papers should follow the Springer’s LNCS format. Papers in PDF can be sent via the EasyChair Conference System Each submission will receive, in addition to a meta-review, at least 2 peer double-blind reviews. Each full paper will get 25 minutes presentation time. Short papers will get 5 minutes presentation time in addition to a poster. In addition to paper presentations, we also plan to have an invited talk by a renowned scientist on a topic relevant for the workshop. Workshop proceedings will be published as part of the ICWE2015 workshop proceedings.
To contact the NLPIT 2015 organization team, please send an e-mail to:


- Submission deadline: April 17, 2015
– Notification deadline: May 17, 2015
– Camera-ready version: May 24, 2015
– Workshop date: June 23, 2015

For more details, NLPIT 2015

International Workshop on Machine learning, Optimization and big Data – MOD 2015

An Interdisciplinary Workshop: Machine Learning, Optimization and Data Science without Borders
Taormina – Sicily, Italy, from July 21 to 24, 2015

The MOD 2015 workshop will consist of one day of tutorials, followed by three days of main workshop sessions. We invite submissions of papers, abstracts and demos on all topics related to Machine learning, Optimization and Big Data including real-world applications for the workshop proceedings ( and proposals for tutorials (

MOD 2015 Paper Format
Please prepare your paper in English using the Lecture Notes in Computer Science (LNCS) template, which is available

Papers must be submitted in PDF.

MOD 2015 Types of Submissions
When submitting a paper to MOD 2015, authors are required to select one of the following four types of papers:
* Long paper: original novel and unpublished work (max. 12 pages in Springer LNCS format);
* Short paper: an extended abstract of novel work (max. 4 pages);
* Work for oral presentation only (no page restriction; any format). For example, work already published elsewhere, which is relevant and which may solicit fruitful discussion at the workshop;
* Work for poster presentation only. The poster format for the presentation is A0 (118.9 cm high and 84.1 cm wide, respectively 46.8 x 33.1 inch).

For research work which is relevant and which may solicit fruitful discussion at the workshop.

MOD 2015 Proceedings
All accepted long papers will be published in a volume of the series ‘Lecture Notes in Computer Science’ from Springer after the Workshop. Instructions for preparing and submitting the final versions (camera-ready papers) of all accepted papers will be available later on.
All the other papers (short papers, abstract of the oral presentations, poster presentations) will be published on the MOD 2015 web site.

Important Dates
Call for Tutorial Submission: February 15, 2015
Tutorial Acceptance: March 1, 2015

Paper Submission: February 28, 2015
Author Notification: April 30, 2015

Call for Late Breaking Papers/Abstracts: April 15, 2015
Author Notification: April 30, 2015

Call for Late Breaking Posters/Demos: May 15, 2015
Author Notification: May 30, 2015

Camera Ready Paper Submission: May 15, 2015
Registration as Presenting Author: May 15, 2015
Early registration: May 15, 2015

Late registration: May 16 – July 24, 2015

On-Site registration: July 21-24, 2015

Workshop: July 21-24, 2015

Sentiment Analysis Symposium- July 15-16, 2015

The Sentiment Analysis Symposium 2015 in New York bridges technologies and business in a unique opportunity to discover value in opinions, emotions, and attitudes in social media, news, and enterprise feedback, to further your business goals.

Call for Speakers is open through January 23, 2015. Please submit your proposal for a presentation (20 or 30 minutes), 5-minute lightning talk, or workshop (60 or 90 minutes) today.

For more details, visit Sentiment Analysis Symposium

Sentiment Analysis of 11 Million Tweets-Going Beyond Positive and Negative

Whether you are analyzing Tweets, Articles or Reviews the overall sentiment and knowing whether it is positive, negative or neutral is cool and useful to know. However, Sentiment Analysis gets a lot more valuable and interesting when we can identify what aspects of entities in particular are positive or negative.

Read the full article: Sentiment Analysis of 11 million Tweets

Stanford algorithm analyzes sentence sentiment, advances machine learning

NaSent is a powerful new ‘recursive deep learning’ algorithm that gives machines the ability to understand how words form meaning in context.

People express opinions every day on issues large and small. Whether the topic is politics, fashion or films, we often rate situations and experiences on a sliding scale of sentiment ranging from thumbs up to thumbs down.

As we increasingly share these opinions via social networks, one result is the creation of vast reservoirs of sentiment that could, if systematically analyzed, provide clues about our collective likes and dislikes with regard to products, personalities and issues.

Against this backdrop, Stanford computer scientists have created a software system that analyzes sentences from movie reviews and gauges the sentiments they express on a five-point scale from strong like to strong dislike.

To read the full article, go to Stanford Algorithm Analyzes Sentence Sentiment

Traders turn to Twitter for market news. Now they can measure mood too.

Financial firms once blocked Twitter to prevent employees leaking sensitive information. It worked, but there was a problem: reporters and companies were using Twitter to reveal important news, leaving traders to rely on second-hand accounts.
Sentiment analysis tool not only alerts traders of a spike an activity, but also shows if the news is likely to be bad or good.
Read more at: Traders turn to Twitter for market news.

Trading On Sentiment Analysis – A Public Relations Tool Goes To Wall Street

Sentiment Analysis has reached the financial arena. Accoridng to a recent publication in FORBES “Thomson Reuters doesn’t just publish news — it now reads the news as well, measures sentiment with sophisticated linguistic analytics and distributes the results to both algorithmic trading engines and real live human traders.”

Read the full article at Trading On Sentiment Analysis in