Ego-state Estimation from Short Texts Based on Sentence Distributed Representation

Human personality multilaterally consists of complex elements. Egogram is a method to classify personalities into patterns according to combinations of five levels of ego-states. With the recent development of Social Networking Services (SNS), a number of studies have attempted to judge personality from statements appearing on various social networking sites. However, there are several problems associated with personality judgment based on the superficial information found in such statements. For example, one's personality is not always reflected in every statement that one makes, and statements are influenced by a personality that tends to change over time. It is also important to collect sufficient amounts of statement data including the results of personality judgments. In this paper, to produce an automatic egogram judgment, we focused on the short texts found on certain SNS sites, especially microblogs. We represented Twitter user comments with a distributed representation (sentence vector) in pre-training and then sought to create a model to estimate the ego-state levels of each Twitter user using a deep neural network. Experimental results showed that our proposed method estimated ego-states with higher accuracy than the baseline method based on bag of words. To investigate changes of personality over time, we analyzed how the match rates of the estimation results changed before/after the egogram judgment. Moreover, we confirmed that the personality pattern classification was improved by adding a feature expressing the degree of formality of the sentence

Computational personality recognition in social media

A variety of approaches have been recently proposed to automatically infer users' personality from their user generated content in social media. Approaches differ in terms of the machine learning algorithms and the feature sets used, type of utilized footprint, and the social media environment used to collect the data. In this paper, we perform a comparative analysis of state-of-the-art computational personality recognition methods on a varied set of social media ground truth data from Facebook, Twitter and YouTube. We answer three questions: (1) Should personality prediction be treated as a multi-label prediction task (i.e., all personality traits of a given user are predicted at once), or should each trait be identified separately? (2) Which predictive features work well across different on-line environments? (3) What is the decay in accuracy when porting models trained in one social media environment to another

Sentiment Analysis for Social Media

Sentiment analysis is a branch of natural language processing concerned with the study of the intensity of the emotions expressed in a piece of text. The automated analysis of the multitude of messages delivered through social media is one of the hottest research fields, both in academy and in industry, due to its extremely high potential applicability in many different domains. This Special Issue describes both technological contributions to the field, mostly based on deep learning techniques, and specific applications in areas like health insurance, gender classification, recommender systems, and cyber aggression detection

Social analytics for health integration, intelligence, and monitoring

Nowadays, patient-generated social health data are abundant and Healthcare is changing from the authoritative provider-centric model to collaborative and patient-oriented care. The aim of this dissertation is to provide a Social Health Analytics framework to utilize social data to solve the interdisciplinary research challenges of Big Data Science and Health Informatics. Specific research issues and objectives are described below. The first objective is semantic integration of heterogeneous health data sources, which can vary from structured to unstructured and include patient-generated social data as well as authoritative data. An information seeker has to spend time selecting information from many websites and integrating it into a coherent mental model. An integrated health data model is designed to allow accommodating data features from different sources. The model utilizes semantic linked data for lightweight integration and allows a set of analytics and inferences over data sources. A prototype analytical and reasoning tool called “Social InfoButtons” that can be linked from existing EHR systems is developed to allow doctors to understand and take into consideration the behaviors, patterns or trends of patients’ healthcare practices during a patient’s care. The tool can also shed insights for public health officials to make better-informed policy decisions. The second objective is near-real time monitoring of disease outbreaks using social media. The research for epidemics detection based on search query terms entered by millions of users is limited by the fact that query terms are not easily accessible by non-affiliated researchers. Publically available Twitter data is exploited to develop the Epidemics Outbreak and Spread Detection System (EOSDS). EOSDS provides four visual analytics tools for monitoring epidemics, i.e., Instance Map, Distribution Map, Filter Map, and Sentiment Trend to investigate public health threats in space and time. The third objective is to capture, analyze and quantify public health concerns through sentiment classifications on Twitter data. For traditional public health surveillance systems, it is hard to detect and monitor health related concerns and changes in public attitudes to health-related issues, due to their expenses and significant time delays. A two-step sentiment classification model is built to measure the concern. In the first step, Personal tweets are distinguished from Non-Personal tweets. In the second step, Personal Negative tweets are further separated from Personal Non-Negative tweets. In the proposed classification, training data is labeled by an emotion-oriented, clue-based method, and three Machine Learning models are trained and tested. Measure of Concern (MOC) is computed based on the number of Personal Negative sentiment tweets. A timeline trend of the MOC is also generated to monitor public concern levels, which is important for health emergency resource allocations and policy making. The fourth objective is predicting medical condition incidence and progression trajectories by using patients’ self-reported data on PatientsLikeMe. Some medical conditions are correlated with each other to a measureable degree (“comorbidities”). A prediction model is provided to predict the comorbidities and rank future conditions by their likelihood and to predict the possible progression trajectories given an observed medical condition. The novel models for trajectory prediction of medical conditions are validated to cover the comorbidities reported in the medical literature

Survey of the State of the Art in Natural Language Generation: Core tasks, applications and evaluation

This paper surveys the current state of the art in Natural Language Generation (NLG), defined as the task of generating text or speech from non-linguistic input. A survey of NLG is timely in view of the changes that the field has undergone over the past decade or so, especially in relation to new (usually data-driven) methods, as well as new applications of NLG technology. This survey therefore aims to (a) give an up-to-date synthesis of research on the core tasks in NLG and the architectures adopted in which such tasks are organised; (b) highlight a number of relatively recent research topics that have arisen partly as a result of growing synergies between NLG and other areas of artificial intelligence; (c) draw attention to the challenges in NLG evaluation, relating them to similar challenges faced in other areas of Natural Language Processing, with an emphasis on different evaluation methods and the relationships between them.Comment: Published in Journal of AI Research (JAIR), volume 61, pp 75-170. 118 pages, 8 figures, 1 tabl

FINE-GRAINED EMOTION DETECTION IN MICROBLOG TEXT

Automatic emotion detection in text is concerned with using natural language processing techniques to recognize emotions expressed in written discourse. Endowing computers with the ability to recognize emotions in a particular kind of text, microblogs, has important applications in sentiment analysis and affective computing. In order to build computational models that can recognize the emotions represented in tweets we need to identify a set of suitable emotion categories. Prior work has mainly focused on building computational models for only a small set of six basic emotions (happiness, sadness, fear, anger, disgust, and surprise). This thesis describes a taxonomy of 28 emotion categories, an expansion of these six basic emotions, developed inductively from data. This set of 28 emotion categories represents a set of fine-grained emotion categories that are representative of the range of emotions expressed in tweets, microblog posts on Twitter. The ability of humans to recognize these fine-grained emotion categories is characterized using inter-annotator reliability measures based on annotations provided by expert and novice annotators. A set of 15,553 human-annotated tweets form a gold standard corpus, EmoTweet-28. For each emotion category, we have extracted a set of linguistic cues (i.e., punctuation marks, emoticons, emojis, abbreviated forms, interjections, lemmas, hashtags and collocations) that can serve as salient indicators for that emotion category. We evaluated the performance of automatic classification techniques on the set of 28 emotion categories through a series of experiments using several classifier and feature combinations. Our results shows that it is feasible to extend machine learning classification to fine-grained emotion detection in tweets (i.e., as many as 28 emotion categories) with results that are comparable to state-of-the-art classifiers that detect six to eight basic emotions in text. Classifiers using features extracted from the linguistic cues associated with each category equal or better the performance of conventional corpus-based and lexicon-based features for fine-grained emotion classification. This thesis makes an important theoretical contribution in the development of a taxonomy of emotion in text. In addition, this research also makes several practical contributions, particularly in the creation of language resources (i.e., corpus and lexicon) and machine learning models for fine-grained emotion detection in text

TWIN: Personality-based Intelligent Recommender System

This paper presents the Tell me What I Need (TWIN) Personality-based Intelligent Recommender System, the goal of which is to recommend items chosen by like-minded (or twin ) people with similar personality types which we estimate from their writings. In order to produce recommendations it applies the results achieved in the personality from the text recognition research field to Personality-based Recommender System user profile modelling. In this way it creates a bridge between the efforts of automatic personality score estimation from plain text and the field of Intelligent Recommender Systems. The paper describes the TWIN system architecture, and results of the experimentation with the system in the online travelling domain in order to investigate the possibility of providing valuable recommendations of hotels of the TripAdvisor website for like-minded people . The results compare favourably with related experiments, although they demonstrate the complexity of this challenging task.The research work of the third author is partially funded by the WIQ-EI (IRSES grant n. 269180) and DIANA APPLICATIONS (TIN2012-38603-C02-01), and done in the framework of the VLC/Campus Microcluster on Multimodal Interaction in Intelligent Systems.Roshchina, A.; Cardiff, J.; Rosso, P. (2015). TWIN: Personality-based Intelligent Recommender System. Journal of Intelligent and Fuzzy Systems. 28(5):2059-2071. https://doi.org/10.3233/IFS-141484S20592071285Bodapati, A. V. (2008). Recommendation Systems with Purchase Data. Journal of Marketing Research, 45(1), 77-93. doi:10.1509/jmkr.45.1.77Dean, J., & Ghemawat, S. (2008). MapReduce. Communications of the ACM, 51(1), 107. doi:10.1145/1327452.1327492Nageswara Rao, K. (2008). Application Domain and Functional Classification of Recommender Systems—A Survey. DESIDOC Journal of Library & Information Technology, 28(3), 17-35. doi:10.14429/djlit.28.3.174Castro, J., Rodriguez, R. M., & Barranco, M. J. (2013). Weighting of Features in Content-Based Filtering with Entropy and Dependence Measures. International Journal of Computational Intelligence Systems, 7(1), 80-89. doi:10.1080/18756891.2013.859861Cantador, I., Bellogín, A., & Vallet, D. (2010). Content-based recommendation in social tagging systems. Proceedings of the fourth ACM conference on Recommender systems - RecSys ’10. doi:10.1145/1864708.1864756Huang, S. (2011). Designing utility-based recommender systems for e-commerce: Evaluation of preference-elicitation methods. Electronic Commerce Research and Applications, 10(4), 398-407. doi:10.1016/j.elerap.2010.11.003Tkalčič, M., Burnik, U., & Košir, A. (2010). Using affective parameters in a content-based recommender system for images. User Modeling and User-Adapted Interaction, 20(4), 279-311. doi:10.1007/s11257-010-9079-zRentfrow, P. J., & Gosling, S. D. (2003). The do re mi’s of everyday life: The structure and personality correlates of music preferences. Journal of Personality and Social Psychology, 84(6), 1236-1256. doi:10.1037/0022-3514.84.6.1236Elahi, M., Braunhofer, M., Ricci, F., & Tkalcic, M. (2013). Personality-Based Active Learning for Collaborative Filtering Recommender Systems. Lecture Notes in Computer Science, 360-371. doi:10.1007/978-3-319-03524-6_31Tkalcic, M., Odic, A., Kosir, A., & Tasic, J. (2013). Affective Labeling in a Content-Based Recommender System for Images. IEEE Transactions on Multimedia, 15(2), 391-400. doi:10.1109/tmm.2012.2229970Quercia, D., Kosinski, M., Stillwell, D., & Crowcroft, J. (2011). Our Twitter Profiles, Our Selves: Predicting Personality with Twitter. 2011 IEEE Third Int’l Conference on Privacy, Security, Risk and Trust and 2011 IEEE Third Int’l Conference on Social Computing. doi:10.1109/passat/socialcom.2011.26Mairesse, F., Walker, M. A., Mehl, M. R., & Moore, R. K. (2007). Using Linguistic Cues for the Automatic Recognition of Personality in Conversation and Text. Journal of Artificial Intelligence Research, 30, 457-500. doi:10.1613/jair.2349Golbeck, J., Robles, C., & Turner, K. (2011). Predicting personality with social media. Proceedings of the 2011 annual conference extended abstracts on Human factors in computing systems - CHI EA ’11. doi:10.1145/1979742.1979614Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., & Witten, I. H. (2009). The WEKA data mining software. ACM SIGKDD Explorations Newsletter, 11(1), 10. doi:10.1145/1656274.1656278Tausczik, Y. R., & Pennebaker, J. W. (2009). The Psychological Meaning of Words: LIWC and Computerized Text Analysis Methods. Journal of Language and Social Psychology, 29(1), 24-54. doi:10.1177/0261927x09351676Islam, M. J., Wu, Q. M. J., Ahmadi, M., & Sid-Ahmed, M. A. (2007). Investigating the Performance of Naive- Bayes Classifiers and K- Nearest Neighbor Classifiers. 2007 International Conference on Convergence Information Technology (ICCIT 2007). doi:10.1109/iccit.2007.14

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

Marketing of innovations & Innovational marketing

У навчальному посібнику розглядаються нові тенденції розвитку маркетингу в 21-м столітті: маркетинг інновацій, зелений маркетинг, партизанський маркетинг, шокова реклама, нейромаркетинг, інтернет-маркетинг, маркетинг в соціальних медіа, подієвий маркетинг і вірусний маркетинг. Розглянуто переваги та недоліки інноваційних видів маркетингу, конкретні особливості їх застосування сучасних умовах. Навчальний посібник призначений для студентів, аспірантів і викладачів економічних спеціальностей, а також для всіх, хто зацікавлений в сучасному маркетингу.В учебном пособии рассматриваются новые тенденции развития маркетинга в 21 веке: маркетинг инноваций, зеленый маркетинг, партизанский маркетинг, шоковая реклама, нейромаркетинг, интернет-маркетинг, маркетинг в социальных сетях, маркетинг событий и вирусный маркетинг. Рассмотрены преимущества и недостатки инновационных видов маркетинга, конкретные особенности их применения в современных условиях. Пособие предназначено для студентов, аспирантов и преподавателей экономических специальностей, а также для всех, кто интересуется современным маркетингом.The teaching manual deals with new marketing development trends in the 21st century: marketing of innovations, green marketing, guerrilla marketing, shock advertising, neuromarketing, internet marketing, social media marketing, event marketing, and viral marketing. Advantages and disadvantages of innovative kinds of marketing, particular features of their application of modern conditions have been considered. The manual is intended for undergraduates, postgraduates and instructors of economic majors, as well as for everybody who is interested in contemporary marketing

Multilingual sentiment analysis in social media.

252 p.This thesis addresses the task of analysing sentiment in messages coming from social media. The ultimate goal was to develop a Sentiment Analysis system for Basque. However, because of the socio-linguistic reality of the Basque language a tool providing only analysis for Basque would not be enough for a real world application. Thus, we set out to develop a multilingual system, including Basque, English, French and Spanish.The thesis addresses the following challenges to build such a system:- Analysing methods for creating Sentiment lexicons, suitable for less resourced languages.- Analysis of social media (specifically Twitter): Tweets pose several challenges in order to understand and extract opinions from such messages. Language identification and microtext normalization are addressed.- Research the state of the art in polarity classification, and develop a supervised classifier that is tested against well known social media benchmarks.- Develop a social media monitor capable of analysing sentiment with respect to specific events, products or organizations