Improving Spanish Polarity Classification Combining Different Linguistic Resources

Sentiment analysis is a challenging task which is attracting the attention of researchers. However, most of work is only focused on English documents, perhaps due to the lack of linguistic resources for other languages. In this paper, we present several Spanish opinion mining resources in order to develop a polarity classification system. In addition, we propose the combination of different features extracted from each resource in order to train a classifier over two different opinion corpora. We prove that the integration of knowledge from several resources can improve the final Spanish polarity classification system. The good results encourage us to continue developing sentiment resources for Spanish, and studying the combination of features extracted from different resourcesMinisterio de Economía y Competitividad TIN2012-38536-C03-0Junta de Andalucía P11-TIC-7684Universidad de Jaén CEATIC-2013-0

Islamic view towards Bitcoin

This paper proposes to analyze the agent behavior by means of big data extracted from the search engine « Google trends » and Twitter API to visualize the emotions and the manner of thinking about « Bitcoin » in the Islamic context. Two kinds of sentiment measures are constructed. The first is based on the search query of the word « Bitcoin » with religious connotation all over the world from 14/04/2017 to 14/04/2018 in weekly frequency. The second is built on twitter data from 03/04/2018 to 13/04/2018, by using a Bayesian machine learning device exploiting deep natural language processing modules to assign emotions and sentiment orientations. In the next step, the Granger causality analysis is used to investigate the hypothesis that this sentiment causes the volatility and the returns of « Bitcoin ». The results show that, at a first-level that twitter users of the word « Islamic Bitcoin » improve positive sentiment. Secondly, the Twitter sentiment measure has a significant effect on lagged Bitcoin returns and volatility. Furthermore, this sentimental variable Granger causes Bitcoin returns and volatility.  This study contributes to the literature by studying the influence of the doctrinal view towards Bitcoin on his prices dynamics. Knowing that Bitcoin is a new financial asset and there is a large debate on his compliance with sharia

Building layered, multilingual sentiment lexicons at synset and lemma levels

Many tasks related to sentiment analysis rely on sentiment lexicons, lexical resources containing information about the emotional implications of words (e.g., sentiment orientation of words, positive or negative). In this work, we present an automatic method for building lemma-level sentiment lexicons, which has been applied to obtain lexicons for English, Spanish and other three official languages in Spain. Our lexicons are multi-layered, allowing applications to trade off between the amount of available words and the accuracy of the estimations. Our evaluations show high accuracy values in all cases. As a previous step to the lemma-level lexicons, we have built a synset-level lexicon for English similar to SENTIWORDNET 3.0, one of the most used sentiment lexicons nowadays. We have made several improvements in the original SENTIWORDNET 3.0 building method, reflecting significantly better estimations of positivity and negativity, according to our evaluations. The resource containing all the lexicons, ML-SENTICON, is publicly available.Ministerio de Economía y Competitividad TIN2012-38536-C03-0

A novel approach to track public emotions related to epidemics in multilingual data

Emergence of new epidemic and re-appearance of older diseases causes great impact towards public health. Surveys based techniques which are costly and time-consuming are the most popular methods to measure information related to public health and used in decision making. Early monitoring of these epidemics helps in rapid decision making. Social media platforms provide rich source of information related to public health in forms of blogs, tweets, public posts etc., but these data is in unstructured form contains multiple languages words. This research focused on developing an automatic system for detecting public emotions related to epidemics in multilingual unstructured data to gain deeper understanding of public emotions and health related information. This approach gives timely information related to epidemics, corresponding symptoms, prevention techniques and awareness, which can help government and health agencies for rapid decision making. Experimental analysis of data set provides results that significantly beat the baseline term counting methods used for sentiment analysis

Interdisciplinary Approach to Emotion Detection from Text

Emotions not only influence most aspects of cognition and behavior, but also play a prominent role in interaction and communication between people. With current multidimensional research on emotions being vast and varied, all researchers of emotions, both psychologists and linguists alike, agree that emotions are at the core of understanding ourselves and others. As a primary vehicle of communication and interaction, language is the most convenient medium for approaching research on the topic of emotions. Not only is one of the main functions of language the emotive one, but the interplay of emotions and language occurs at all linguistic levels. Textual data, in particular, can be beneficial to emotion detection due to its syntactic and semantic information containing not only informative content, but emotional states as well. A general overview of the emotion models based on the research in psychology, as well as the major approaches to emotion detection from text found in linguistics, together with usage demonstration of emotion detection linguistic tools, will be given in this paper. Examples of useful applications – from psychologists analyzing session transcripts in search for any subtle emotions, over public opinion mining on social networks to the development of AI technology – will also be provided showing that emotion detection from text has an abundance of practical uses. As the methods for emotion detection from text become more accurate, uses and applications of emotion detection from text will become more numerous and diverse in the future

Learning to identify emotions in text

This paper discusses learning to identify emotions in text

Проектирование и программная реализация модуля интерпретации и визуализации результатов интеллектуального контент-анализа веб-сервисов

В рамках выполняемой работы планируется проектирование и разработка модуля для обеспечения отображения результатов работы нейронной сети, являющейся частью комплексного проекта по созданию информационной системы сравнительного анализа общественного мнения на основе данных социальных сетей. Разрабатываемый модуль обеспечит интерпретацию и наглядное представление полученных результатов контент-анализа с целью дальнейшей оценки их значимости.Within the framework of the work performed, it is planned to design and develop a module to ensure the display of the results of the work of the neural network, which is part of a comprehensive project to create an information system for comparative analysis of public opinion based on social networking data. The module being developed will provide interpretation and visual presentation of the results of the content analysis with a view to further assessing their significance