448 research outputs found

    Affective computing for smart operations: a survey and comparative analysis of the available tools, libraries and web services

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    In this paper, we make a deep search of the available tools in the market, at the current state of the art of Sentiment Analysis. Our aim is to optimize the human response in Datacenter Operations, using a combination of research tools, that allow us to decrease human error in general operations, managing Complex Infrastructures. The use of Sentiment Analysis tools is the first step for extending our capabilities for optimizing the human interface. Using different data collections from a variety of data sources, our research provides a very interesting outcome. In our final testing, we have found that the three main commercial platforms (IBM Watson, Google Cloud and Microsoft Azure) get the same accuracy (89-90%). for the different datasets tested, based on Artificial Neural Network and Deep Learning techniques. The other stand-alone Applications or APIs, like Vader or MeaninCloud, get a similar accuracy level in some of the datasets, using a different approach, semantic Networks, such as Concepnet1, but the model can easily be optimized above 90% of accuracy, just adjusting some parameter of the semantic model. This paper points to future directions for optimizing DataCenter Operations Management and decreasing human error in complex environments

    Words of Experience: Semantic Content Analysis and Individual Differences among Successful Second Language Learners

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    Individual differences (IDs) in second language (L2) learning have traditionally been studied as separate, isolated variables (Dörnyei, 2005), but this reductionist approach has led to a fragmented and inconclusive understanding of how IDs influence L2 learning. The present study takes a different approach to IDs by starting at the level of L2 learning experience and identifying the most basic differences between learners. To do this, a new L2 experience methodology is introduced. Participants are 123 matriculated non-native English speaking students at two urban universities in the South. First, learners were interviewed following a strict interview protocol which ensured that all learners received the same input in the same setting. Next, the interviews were analyzed using the Linguistic Inquiry and Word Count software (Pennebaker, Booth, & Francis, 2007), which provides quantitative output showing the types and frequency of psychosocial words each learner produced. These psychosocial semantic categories then formed the basis of a cluster analysis that identified groups of learners who use similar semantic categories. Learners who tend to use similar psychosocial words to describe their L2 learning experience are assumed to share a similar approach to L2 learning and are grouped together into L2 learning profiles. Results show that these participants can be grouped into three types of successful L2 experiences: Doing, Thinking, and Feeling. An ANOVA and follow-up ad hoc statistical tests reveal significant differences in admissions TOEFL scores among these groups of students, suggesting that learners who describe their L2 experience differently do in fact show significant differential performance. Qualitative analysis of interview transcripts further suggests that the influence of family plays an important role in differential TOEFL scores, and that L2 learning experience may change in important ways over time. Based on the results of the study, a L2 Experience Model of Individual and Social Differences is proposed that accounts for life importance, effort, ability, and L2 experience. Implications of this new methodology and model are discussed, along with suggestions for future research, teaching, and L2 learning

    LANGUAGE STRATEGIES IN INTERNATIONAL BUSINESS: NEW PROSPECTS FOR NEGOTIATION AND CONFLICT MANAGEMENT

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    With the COVID-19 pandemic of 2020 — when negotiations have been almost exclusively carried out in online settings — there is a growing need for research which addresses this new norm. This dissertation explores how linguistic cues can corroborate or challenge the established measures in negotiation and conflict management research. The overarching objective is to study the interdependence of language and culture in the presence of technology within the domain of international negotiations and conflict resolution. The first essay of the dissertation addresses the anomalies regarding the use of the two major negotiation strategies identified by prior research – questions and answers (Q&A) and substantiation and offers (S&O) – and their effectiveness across cultures. I triangulate between cognitive methods utilized in negotiations research (mental model convergence, fixed-pie bias), linguistic cues (words with positive and negative connotations), and language style matching (LSM), a novel analysis in international buyer-seller negotiations. Based on an online negotiation simulation between representatives of a high-context (Hong Kong Chinese) and low-context (U.S.) communication culture (total sample size is 300) and subsequent linguistic analysis of the transcripts, the essay questions the notion of normative strategy; shows the conditions when the strategies have an integrative versus distributive character; identifies cognitive mechanisms which explain why S&O might be more beneficial than Q&A in a high-context communication culture; and clarifies in which cultural contexts the index of language style matching reflects a deeper, cognitive simmilarity and in which an automatic process. The second essay is a systematic literature review of studies about language in international conflict management research. The essay emphasizes a positive potential of a conflict and suggests how it can be achieved linguistically in an intercultural environment. It shows how language can give a dynamic process to conflict management. Unlike the static view of conflict, the proposed theoretical framework underscores the importance of poly-contextual behavior, i.e., how the behavior changes across contexts. By focusing on the multilingualism, the essay further disentangles language and culture, which are often mixed together. The essay suggests short term and long term strategies for a dynamic conflict de-escalation in the domain of international business

    Lingualyzer: A computational linguistic tool for multilingual and multidimensional text analysis

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    Most natural language models and tools are restricted to one language, typically English. For researchers in the behavioral sciences investigating languages other than English, and for those researchers who would like to make cross-linguistic comparisons, hardly any computational linguistic tools exist, particularly none for those researchers who lack deep computational linguistic knowledge or programming skills. Yet, for interdisciplinary researchers in a variety of fields, ranging from psycholinguistics, social psychology, cognitive psychology, education, to literary studies, there certainly is a need for such a cross-linguistic tool. In the current paper, we present Lingualyzer (https://lingualyzer.com), an easily accessible tool that analyzes text at three different text levels (sentence, paragraph, document), which includes 351 multidimensional linguistic measures that are available in 41 different languages. This paper gives an overview of Lingualyzer, categorizes its hundreds of measures, demonstrates how it distinguishes itself from other text quantification tools, explains how it can be used, and provides validations. Lingualyzer is freely accessible for scientific purposes using an intuitive and easy-to-use interface

    Temperament detection based on Twitter data: classical machine learning versus deep learning

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    Deep learning has shown promising results in various text-based classification tasks. However, deep learning performance is affected by the number of data, i.e., when the number of data is small, deep learning algorithms do not perform well, and vice versa. Classical machine learning algorithms commonly work well for a few data, and their performance reaches an optimal value and does not increase with the increase in sample data. Therefore, this study aimed to compare the performance of classical machine learning and deep learning methods to detect temperament based on Indonesian Twitter. In this study, the proposed Indonesian Linguistic Inquiry and Word Count were employed to analyze the context of Twitter. The classical machine learning methods implemented were support vector machine and K-nearest neighbor, whereas the deep learning method employed was a convolutional neural network (CNN) with three different architectures. Both learning methods were implemented using multiclass classification and one versus all (OVA) multiclass classification. The highest average f-measure was 58.73%, obtained by CNN OVA with a pool size of 3, a dropout value of 0.7, and a learning rate value of 0.0007

    Deception Detection Using Machine Learning

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    Today’s digital society creates an environment potentially conducive to the exchange of deceptive information. The dissemination of misleading information can have severe consequences on society. This research investigates the possibility of using shared characteristics among reviews, news articles, and emails to detect deception in text-based communication using machine learning techniques. The experiment discussed in this paper examines the use of Bag of Words and Part of Speech tag features to detect deception on the aforementioned types of communication using Neural Networks, Support Vector Machine, Naïve Bayesian, Random Forest, Logistic Regression, and Decision Tree. The contribution of this paper is two-fold. First, it provides initial insight into the identification of text communication cues useful in detecting deception across different types of text-based communication. Second, it provides a foundation for future research involving the application of machine learning algorithms to detect deception on different types of text communication
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