Comparing and Combining Sentiment Analysis Methods

Several messages express opinions about events, products, and services, political views or even their author's emotional state and mood. Sentiment analysis has been used in several applications including analysis of the repercussions of events in social networks, analysis of opinions about products and services, and simply to better understand aspects of social communication in Online Social Networks (OSNs). There are multiple methods for measuring sentiments, including lexical-based approaches and supervised machine learning methods. Despite the wide use and popularity of some methods, it is unclear which method is better for identifying the polarity (i.e., positive or negative) of a message as the current literature does not provide a method of comparison among existing methods. Such a comparison is crucial for understanding the potential limitations, advantages, and disadvantages of popular methods in analyzing the content of OSNs messages. Our study aims at filling this gap by presenting comparisons of eight popular sentiment analysis methods in terms of coverage (i.e., the fraction of messages whose sentiment is identified) and agreement (i.e., the fraction of identified sentiments that are in tune with ground truth). We develop a new method that combines existing approaches, providing the best coverage results and competitive agreement. We also present a free Web service called iFeel, which provides an open API for accessing and comparing results across different sentiment methods for a given text.Comment: Proceedings of the first ACM conference on Online social networks (2013) 27-3

The typical developmental trajectory of social and executive functions in late adolescence and early adulthood.

Executive functions and social cognition develop through childhood into adolescence/early adulthood and are important for adaptive goal-oriented behaviour (Apperly, Samson & Humphreys, 2009; Blakemore & Choudhury, 2006). These functions are attributed to frontal networks known to undergo protracted maturation into early adulthood (Barker, Andrade, Morton, Romanowski & Bowles, 2010; Lebel, Walker, Leemans, Phillips & Beaulieu, 2008) although social cognition functions are also associated with widely distributed networks. Previously, non-linear development has been reported around puberty on an emotion match to sample task (McGivern, Andersen, Byrd, Mutter & Reilly, 2002) and for IQ in mid adolescence (Ramsden et al., 2011). However, there are currently little data on the typical development of social and executive functions in late adolescence and early adulthood. In a cross sectional design, 98 participants completed tests of social cognition and executive function, Wechsler Abbreviated Scale of Intelligence (Wechsler, 1999), Positive and Negative Affect Scale (Watson, Clark & Tellegan, 1988), Hospital Anxiety and Depression Scale (Zigmond & Snaith, 1983) and measures of pubertal development and demographics at age 17, 18 and 19. Non-linear age differences for letter fluency and concept formation executive functions were found, with a trough in functional ability in 18 year olds compared to other groups. There were no age group differences on social cognition measures. Gender accounted for differences on one scale of concept formation, one dynamic social interaction scale and two empathy scales. The clinical, developmental and educational implications of these findings are discussed

Measuring Social Well Being in The Big Data Era: Asking or Listening?

The literature on well being measurement seems to suggest that "asking" for a self-evaluation is the only way to estimate a complete and reliable measure of well being. At the same time "not asking" is the only way to avoid biased evaluations due to self-reporting. Here we propose a method for estimating the welfare perception of a community simply "listening" to the conversations on Social Network Sites. The Social Well Being Index (SWBI) and its components are proposed through to an innovative technique of supervised sentiment analysis called iSA which scales to any language and big data. As main methodological advantages, this approach can estimate several aspects of social well being directly from self-declared perceptions, instead of approximating it through objective (but partial) quantitative variables like GDP; moreover self-perceptions of welfare are spontaneous and not obtained as answers to explicit questions that are proved to bias the result. As an application we evaluate the SWBI in Italy through the period 2012-2015 through the analysis of more than 143 millions of tweets.Comment: 40 pages, 2 figures. arXiv admin note: text overlap with arXiv:1512.0156

Using network analysis for the prediction of treatment dropout in patients with mood and anxiety disorders: a methodological proof-of-concept study

There are large health, societal, and economic costs associated with attrition from psychological services. The recently emerged, innovative statistical tool of complex network analysis was used in the present proof-of-concept study to improve the prediction of attrition. Fifty-eight patients undergoing psychological treatment for mood or anxiety disorders were assessed using Ecological Momentary Assessments four times a day for two weeks before treatment (3,248 measurements). Multilevel vector autoregressive models were employed to compute dynamic symptom networks. Intake variables and network parameters (centrality measures) were used as predictors for dropout using machine-learning algorithms. Networks for patients differed significantly between completers and dropouts. Among intake variables, initial impairment and sex predicted dropout explaining 6% of the variance. The network analysis identified four additional predictors: Expected force of being excited, outstrength of experiencing social support, betweenness of feeling nervous, and instrength of being active. The final model with the two intake and four network variables explained 32% of variance in dropout and identified 47 out of 58 patients correctly. The findings indicate that patients’ dynamic network structures may improve the prediction of dropout. When implemented in routine care, such prediction models could identify patients at risk for attrition and inform personalized treatment recommendations.This work was supported by the German Research Foundation National Institute (DFG, Grant nos. LU 660/8-1 and LU 660/10-1 to W. Lutz). The funder of the study had no role in study design, data collection, data analysis, data interpretation, or writing of the manuscript. The corresponding author had access to all data in the study and had final responsibility for the decision to submit for publication. Dr. Hofmann receives financial support from the Alexander von Humboldt Foundation (as part of the Humboldt Prize), NIH/NCCIH (R01AT007257), NIH/NIMH (R01MH099021, U01MH108168), and the James S. McDonnell Foundation 21st Century Science Initiative in Understanding Human Cognition - Special Initiative. (LU 660/8-1 - German Research Foundation National Institute (DFG); LU 660/10-1 - German Research Foundation National Institute (DFG); Alexander von Humboldt Foundation; R01AT007257 - NIH/NCCIH; R01MH099021 - NIH/NIMH; U01MH108168 - NIH/NIMH; James S. McDonnell Foundation 21st Century Science Initiative in Understanding Human Cognition - Special Initiative)Accepted manuscrip

Predicting Product Performance with Social Media

Last 20 years brought massive growth in IT&C world. Mobile solutions such as netbooks, laptops, mobile phones, tablets enable the wireless connection to the Internet. Anyone can ac-cess it anytime and anywhere. In this context, a part of the activities from the real world have a correspondence in the online discussions. Social media in general and social networks in particular have turned into marketing tools for organizations and a place where people can express their opinions and attitudes about products.The paper shows how social media can be used for predicting the success of a product or service. To showcase this, two case studies are presented; a test to prove that the conversations that take place in social media are a good indicator of success and the second is an exercise to predict the winner of the Oscar for best picture in 2011.Social Media, Social Networks, Prediction, Movie, Internet

Happy but still focused: failures to find evidence for a mood-induced widening of visual attention

In models of affect and cognition it is held that positive affect broadens the scope of attention. Consistent with this claim, previous research has indeed suggested that positive affect is associated with impaired selective attention as evidenced by increased interference of spatially distant distractors. However, several recent findings cast doubt on the reliability of this observation. In the present study we examined whether selective attention in a visual flanker task is influenced by positive mood induction. Across three experiments, positive affect consistently failed to exert any impact on selective attention. The implications of this null-finding for theoretical models of affect and cognition are discussed