HCOOCH3 as a probe of temperature and structure of Orion-KL

We studied the O-bearing molecule HCOOCH3 to characterize the physical conditions of the different molecular source components in Orion-KL. We identify 28 methyl formate emission peaks throughout the 50" field of observations. The two strongest peaks are in the Compact Ridge (MF1) and in the SouthWest of the Hot Core (MF2). Spectral confusion is still prevailing as half of the expected transitions are blended over the region. Assuming that the transitions are thermalized, we derive the temperature at the five main emission peaks. At the MF1 position we find a temperature of 80K in a 1.8"x0.8" beam size and 120K on a larger scale (3.6" x2.2"), suggesting an external source of heating, whereas the temperature is about 130K at the MF2 position on both scales. Transitions of HCOOCH3 in vt=1 are detected as well and the good agreement of the positions on the rotational diagrams between the vt=0 and the vt=1 transitions suggests a similar temperature. The velocity of the gas is between 7.5 and 8.0km/s depending on the positions and column density peaks vary from 1.6x10^16 to 1.6x10^17cm^-2. A second velocity component is observed around 9-10 km/s in a North-South structure stretching from the Compact Ridge up to the BN object; this component is warmer at the MF1 peak. The two other C2H4O2 isomers are not detected and the derived upper limit for the column density is <3x10^14cm^-2 for glycolaldehyde and <2x10^15cm^-2 for acetic acid. From the 223GHz continuum map, we identify several dust clumps with associated gas masses in the range 0.8 to 5.8Msun. Assuming that the HCOOCH3 is spatially distributed as the dust, we find relative abundances of HCOOCH3 in the range <0.1x10^-8 to 5.2x10^-8. We suggest a relation between the methyl formate distribution and shocks as traced by 2.12 mum H2 emission.Comment: Accepted for publication in A&

Precise dipole moment and quadrupole coupling constants of benzonitrile

We have performed Fourier transform microwave spectroscopy of benzonitrile, without and with applied electric fields. From the field-free hyperfine-resolved microwave transitions we simultaneously derive accurate values for the rotational constants, centrifugal distortion constants, and nitrogen nuclear quadrupole coupling constants of benzonitrile. By measuring the Stark shift of selected hyperfine transitions the electric dipole moment of benzonitrile is determined to $\mu=\mu_a=4.5152 (68)$ D.Comment: 6 pages, 2 tables (elsart

Exploring the value of big data analysis of Twitter tweets and share prices

Over the past decade, the use of social media (SM) such as Facebook, Twitter, Pinterest and Tumblr has dramatically increased. Using SM, millions of users are creating large amounts of data every day. According to some estimates ninety per cent of the content on the Internet is now user generated. Social Media (SM) can be seen as a distributed content creation and sharing platform based on Web 2.0 technologies. SM sites make it very easy for its users to publish text, pictures, links, messages or videos without the need to be able to program. Users post reviews on products and services they bought, write about their interests and intentions or give their opinions and views on political subjects. SM has also been a key factor in mass movements such as the Arab Spring and the Occupy Wall Street protests and is used for human aid and disaster relief (HADR). There is a growing interest in SM analysis from organisations for detecting new trends, getting user opinions on their products and services or finding out about their online reputation. Companies such as Amazon or eBay use SM data for their recommendation engines and to generate more business. TV stations buy data about opinions on their TV programs from Facebook to find out what the popularity of a certain TV show is. Companies such as Topsy, Gnip, DataSift and Zoomph have built their entire business models around SM analysis. The purpose of this thesis is to explore the economic value of Twitter tweets. The economic value is determined by trying to predict the share price of a company. If the share price of a company can be predicted using SM data, it should be possible to deduce a monetary value. There is limited research on determining the economic value of SM data for “nowcasting”, predicting the present, and for forecasting. This study aims to determine the monetary value of Twitter by correlating the daily frequencies of positive and negative Tweets about the Apple company and some of its most popular products with the development of the Apple Inc. share price. If the number of positive tweets about Apple increases and the share price follows this development, the tweets have predictive information about the share price. A literature review has found that there is a growing interest in analysing SM data from different industries. A lot of research is conducted studying SM from various perspectives. Many studies try to determine the impact of online marketing campaigns or try to quantify the value of social capital. Others, in the area of behavioural economics, focus on the influence of SM on decision-making. There are studies trying to predict financial indicators such as the Dow Jones Industrial Average (DJIA). However, the literature review has indicated that there is no study correlating sentiment polarity on products and companies in tweets with the share price of the company. The theoretical framework used in this study is based on Computational Social Science (CSS) and Big Data. Supporting theories of CSS are Social Media Mining (SMM) and sentiment analysis. Supporting theories of Big Data are Data Mining (DM) and Predictive Analysis (PA). Machine learning (ML) techniques have been adopted to analyse and classify the tweets. In the first stage of the study, a body of tweets was collected and pre-processed, and then analysed for their sentiment polarity towards Apple Inc., the iPad and the iPhone. Several datasets were created using different pre-processing and analysis methods. The tweet frequencies were then represented as time series. The time series were analysed against the share price time series using the Granger causality test to determine if one time series has predictive information about the share price time series over the same period of time. For this study, several Predictive Analytics (PA) techniques on tweets were evaluated to predict the Apple share price. To collect and analyse the data, a framework has been developed based on the LingPipe (LingPipe 2015) Natural Language Processing (NLP) tool kit for sentiment analysis, and using R, the functional language and environment for statistical computing, for correlation analysis. Twitter provides an API (Application Programming Interface) to access and collect its data programmatically. Whereas no clear correlation could be determined, at least one dataset was showed to have some predictive information on the development of the Apple share price. The other datasets did not show to have any predictive capabilities. There are many data analysis and PA techniques. The techniques applied in this study did not indicate a direct correlation. However, some results suggest that this is due to noise or asymmetric distributions in the datasets. The study contributes to the literature by providing a quantitative analysis of SM data, for example tweets about Apple and its most popular products, the iPad and iPhone. It shows how SM data can be used for PA. It contributes to the literature on Big Data and SMM by showing how SM data can be collected, analysed and classified and explore if the share price of a company can be determined based on sentiment time series. It may ultimately lead to better decision making, for instance for investments or share buyback

What the future holds for social media data analysis

The dramatic rise in the use of Social Media (SM) platforms such as Facebook and Twitter provide access to an unprecedented amount of user data. Users may post reviews on products and services they bought, write about their interests, share ideas or give their opinions and views on political issues. There is a growing interest in the analysis of SM data from organisations for detecting new trends, obtaining user opinions on their products and services or finding out about their online reputations. A recent research trend in SM analysis is making predictions based on sentiment analysis of SM. Often indicators of historic SM data are represented as time series and correlated with a variety of real world phenomena like the outcome of elections, the development of financial indicators, box office revenue and disease outbreaks. This paper examines the current state of research in the area of SM mining and predictive analysis and gives an overview of the analysis methods using opinion mining and machine learning techniques

Temporal entrainment in overlapping speech

Wlodarczak M. Temporal entrainment in overlapping speech. Bielefeld: Bielefeld University; 2014

Context Cues For Classification Of Competitive And Collaborative Overlaps

Being able to respond appropriately to users’ overlaps should be seen as one of the core competencies of incremental dialogue systems. At the same time identifying whether an interlocutor wants to support or grab the turn is a task which comes naturally to humans, but has not yet been implemented in such systems. Motivated by this we first investigate whether prosodic characteristics of speech in the vicinity of overlaps are significantly different from prosodic characteristics in the vicinity of non-overlapping speech. We then test the suitability of different context sizes, both preceding and following but excluding features of the overlap, for the automatic classification of collaborative and competitive overlaps. We also test whether the fusion of preceding and succeeding contexts improves the classification. Preliminary results indicate that the optimal context for classification of overlap lies at 0.2 seconds preceding the overlap and up to 0.3 seconds following it. We demonstrate that we are able to classify collaborative and competitive overlap with a median accuracy of 63%

What the future holds for social media data analysis

The dramatic rise in the use of Social Media (SM) platforms such as Facebook and Twitter provide access to an unprecedented amount of user data. Users may post reviews on products and services they bought, write about their interests, share ideas or give their opinions and views on political issues. There is a growing interest in the analysis of SM data from organisations for detecting new trends, obtaining user opinions on their products and services or finding out about their online reputations. A recent research trend in SM analysis is making predictions based on sentiment analysis of SM. Often indicators of historic SM data are represented as time series and correlated with a variety of real world phenomena like the outcome of elections, the development of financial indicators, box office revenue and disease outbreaks. This paper examines the current state of research in the area of SM mining and predictive analysis and gives an overview of the analysis methods using opinion mining and machine learning techniques