Метод межъязыкового аспектно-ориентированного анализа высказываний с использованием машинного обучение категоризационной модели.

Product reviews are the foremost source of information for customers and manufacturers to help them make appropriate purchasing and production decisions. Today, the Internet has become the largest source of consumer thought. Sentiment analysis and opinion mining is the field of study that analyzes people’s opinions, sentiments, evaluations, attitudes, and emotions from written language. In this paper, we present a study of aspect-based opinion mining using a lexicon-based approach and their adaptation to the processing of responses written in Ukrainian and English. This information helps to build systems to understand customer’s feedback and plan business strategies accordingly. This also helps in predicting the chances of product failure. In this paper, it is explained how machine learning can be used for opinion mining. The research methods used in the work are based on data mining methods, Web mining, machine learning, and information retrieval. The stages of the method of cross-language aspect-oriented analysis of statements are presented. The cross-language categorization of characteristics of goods is considered. The algorithm describes the model learning in cross-language virtual contextual documents.Відгуки про продукцію є головним джерелом інформації для клієнтів і виробників, щоб допомогти їм прийняти відповідні рішення щодо закупівель і виробництва. Сьогодні Інтернет став найбільшим джерелом споживчої думки. Аналіз настроїв і видобування думок є сферою дослідження, яка аналізує думки людей, почуття, оцінки, ставлення та емоції з природно-мовного тексту. У даній роботі представлено дослідження аспектно-орієнтованого видобування думок з використанням лексіконного підходу та його адаптація до обробки відповідей, написаних українською та англійською мовами. Ця інформація допомагає створювати системи для розуміння зворотного зв'язку клієнта та планування відповідних бізнес-стратегій. Це також допомагає прогнозувати шляхи запобігання невдач при просуванні на ринку продуктів. У цій роботі розглянуто використання машинного навчання для видобутку думок клієнтів. Методи дослідження, що використовуються в роботі, базуються на методах інтелектуального аналізу даних, веб-добуванні, машинному навчанні та пошуку інформації. Представлено етапи методу міжмовного аспектно-орієнтованого аналізу тверджень. Розглянуто перехресну категоризацію характеристик товарів. Алгоритм описує модель навчання на міжмовному віртуальному контекстному документі.Отзывы о продукции является главным источником информации для клиентов и производителей, чтобы помочь им принять соответствующие решения в части закупок и производства. Сегодня Интернет стал крупнейшим источником потребительского мнения. Анализ настроений и выявления мыслей является сферой исследования, которая анализирует мнения людей, чувства, оценки, отношения и эмоции с естественно-языкового текста. В данной работе представлено исследование аспектно-ориентированного выявления мыслей с использованием лексиконного подхода и его адаптация к обработки ответов, написанных на украинском и английском языках. Эта информация помогает создавать системы для понимания обратной связи клиента и планирования соответствующих бизнес-стратегий. Это также помогает прогнозировать пути предотвращения неудач при продвижении на рынке продуктов. В этой работе рассмотрено использование машинного обучения для выявления мнений клиентов. Методы исследования, используемые в работе, базируются на методах интеллектуального анализа данных, веб-добывании, машинном обучении и поиска информации. Представлены этапы метода межъязыкового аспектно-ориентированного анализа утверждений. Рассмотрена перекрестная категоризацию характеристик товаров. Алгоритм описывает модель обучения на межъязыковой виртуальном контекстном документе

Resource Creation and Evaluation for Multilingual Sentiment Analysis in Social Media Texts

Sentiment analysis (SA) regards the classification of texts according to the polarity of the opinions they express. SA systems are highly relevant to many real-world applications (e.g. marketing, eGovernance, business intelligence, behavioral sciences) and also to many tasks in Natural Language Processing (NLP) – information extraction, question answering, textual entailment, to name just a few. The importance of this field has been proven by the high number of approaches proposed in research, as well as by the interest that it raised from other disciplines and the applications that were created using its technology. In our case, the primary focus is to use sentiment analysis in the context of media monitoring, to enable tracking of global reactions to events. The main challenge that we face is that tweets are written in different languages and an unbiased system should be able to deal with all of them, in order to process all (possible) available data. Unfortunately, although many linguistic resources exist for processing texts written in English, for many other languages data and tools are scarce. Following our initial efforts described in (Balahur and Turchi, 2013), in this article we extend our study on the possibility to implement a multilingual system that is able to a) classify sentiment expressed in tweets in various languages using training data obtained through machine translation; b) to verify the extent to which the quality of the translations influences the sentiment classification performance, in this case, of highly informal texts; and c) to improve multilingual sentiment classification using small amounts of data annotated in the target language. To this aim, varying sizes of target language data are tested. The languages we explore are: Arabic, Turkish, Russian, Italian, Spanish, German and French.JRC.G.2-Global security and crisis managemen

A Fine-grained Multilingual Analysis Based on the Appraisal Theory: Application to Arabic and English Videos

International audienceThe objective of this paper is to compare the opinions of two videos in two different languages. To do so, a fine-grained approach inspired from the appraisal theory is used to analyze the content of the videos that concern the same topic. In general, the methods devoted to sentiment analysis concern the study of the polarity of a text or an utterance. The appraisal approach goes further than the basic polarity sentiments and consider more detailed sentiments by covering additional attributes of opinions such as: Attitude, Graduation and Engagement. In order to achieve such a comparison, in AMIS (Chist-Era project), we collected a corpus of 1503 Arabic and 1874 English videos. These videos need to be aligned in order to compare their contents, that is why we propose several methods to make them comparable. Then the best one is selected to align them and to constitute the data-set necessary for the fine-grained sentiment analysis

Aplicações da Sentiment Analysis na Gestão de Empresas

O crescimento do digital vai-se evidenciando cada vez mais como sendo um processo irreversível e como tal, os gestores têm de ter capacidade de adaptação e saber identificar oportunidades para tirarem melhor proveito destes recursos tecnológicos. Neste sentido, a sentiment analysis é uma técnica analítica que permite analisar e classificar, através de corpos de textos, a polaridade (positiva, neutra ou negativa) dum determinando assunto. À medida que aumenta o volume de informação textual disponível online, esta técnica tem um grande potencial de aplicação. No entanto, na literatura não se encontram estudos anteriores para determinar quais são as funções de negócios reais que usam a análise de sentimentos (SA), nem em que medida/propósitos as fontes de dados são usadas para tal empreendimento e, portanto, é necessário conhecer as formas de uso da SA na gestão de negócios, para maximizar o seu uso potencial e os benefícios que vêm com ele. Este trabalho tem por objetivo explorar e identificar as temáticas em que são aplicadas a sentiment analysis na gestão de empresas, bem como identificar as principais fontes de dados para fazer este tipo de análise. Estas temáticas foram enquadradas nas cinco funções básicas da gestão: administrativa, financeira, de marketing, de produção e de recursos humanos. Para alcançar isto, o estudo foi conduzido através de revisão sistemática da literatura, conjuntamente com uma análise bibliométrica e a elaboração de uma proposta de taxonomia. Para a realização do estudo, foram extraídos 1.151 artigos da Web of Science, provenientes de periódicos e conferências. Os resultados sugerem que a SA é maioritariamente utilizada nas funções de marketing e na financeira, embora também se verifiquem aplicações nas funções administrativa, produção e recursos humanos, mas de forma residual. Concluiu-se ainda que existem 4 tipos de fontes de informação: documentação interna, documentação financeira, redes sociais/publica e académica.It is increasingly evident that the growth of digital technology is becoming an irreversible process, implying that managers must have the ability to adapt and to identify opportunities to make the most of these technological resources. Therefore, sentiment analysis is an analytical technique that allows the analysis and classification of the polarity (positive, neutral, or negative) of a given subject embedded within a text. As the volume of textual information available online increases, this technique has great potential for application. However, no previous studies were found in the literature to determine which actual business functions are using sentiment analysis (SA), nor to what extent or purpose distinct data sources are being used for such an endeavor. Thus, it is necessary to know how SA is being used in business management, to maximize its potential use and the benefits that come along with it. This paper aims to explore and identify the themes for which sentiment analysis is applied in business management, as well as to identify the main data sources for doing this type of analysis. These themes were framed within the five basic business functions: general management, finance, marketing, production and human resources. To achieve the proposed goals, this study underwent a systematic literature review, used a bibliometric analysis, and developed a taxonomy proposal. To conduct the study, 1.151 articles, whether from journals or conferences, were extracted from Web of Science. The results suggest that the SA is mostly used in marketing and finance, there are also applications regarding the other functions, though in a residual way. It was also concluded that there are 4 types of information sources: academic, internal documentation, financial documentation, and social networks/public information

Evaluating Multilanguage-Comparability of Subjectivity Analysis Systems

Subjectivity analysis is a rapidly growing field of study. Along with its applications to various NLP tasks, much work have put efforts into multilingual subjectivity learning from existing resources. Multilingual subjectivity analysis requires language-independent criteria for comparable outcomes across languages. This paper proposes to measure the multilanguage-comparability of subjectivity analysis tools, and provides meaningful comparisons of multilingual subjectivity analysis from various points of view.11Nsciescopu