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OBOME - Ontology based opinion mining in UBIPOL
Ontologies have a special role in the UBIPOL system, they help to structure the policy related context, provide conceptualization for policy domain and use in the opinion mining process. In this work we presented a system called Ontology Based Opinion Mining Engine (OBOME) for analyzing a domain-specific opinion corpus by first assisting the user with the creation of a domain ontology from the corpus. We determined the polarity of opinion on the various domain aspects. In the former step, the policy domain aspect has are identified (namely which policy category is represented by the concept). This identification is supported by the policy modelling ontology, which describe the most important policy – related classes and structure. Then the most informative documents from the corpus are extracted and asked the user to create a set of aspects and related keywords using these documents. In the latter step, we used the corpus specific ontology to model the domain and extracted aspect-polarity associations using grammatical dependencies between words. Later, summarized results are shown to the user to analyze and store. Finally, in an offline process policy modeling ontology is updated
Ontologies for aspects automatic detection in sentiment analysis /
En este artículo se analiza el papel de las ontologías en los sistemas de Análisis de Sentimientos a nivel de
aspectos. El objetivo de la investigación es indagar sobre las técnicas que se han aplicado en sistemas de análisis
de sentimientos donde se hayan utilizado ontologías ya sea para la extracción de los aspectos o determinación
del sentimiento. Para lograr lo planeado se seleccionaron los trabajos más representativos de la literatura a través
de una revisión sistemática en donde se identiicaron algunos criterios comunes que permitieron un análisis
comparativo de los trabajos versus los criterios. Los resultados obtenidos permiten dar las bases necesarias para
el desarrollo de un modelo de análisis de sentimientos a nivel de aspectos para el español basado en ontologías.ABSTRACT:
In this paper the role of ontologies is discussed in aspects-level sentiment analysis. The objective of the
research is to investigate techniques that have been applied in sentiment analysis systems where ontologies
have been used either for the extraction of the aspects or determination of sentiment. For this were selected
the most representative papers of literature through a systematic review where some common criteria
are identiied which they enabled a comparative analysis of the documents versus the criteria The results
obtained provide the necessary for the development of a model of aspects-level sentiment analysis for the
Spanish based on ontologie
Knowledge will Propel Machine Understanding of Content: Extrapolating from Current Examples
Machine Learning has been a big success story during the AI resurgence. One
particular stand out success relates to learning from a massive amount of data.
In spite of early assertions of the unreasonable effectiveness of data, there
is increasing recognition for utilizing knowledge whenever it is available or
can be created purposefully. In this paper, we discuss the indispensable role
of knowledge for deeper understanding of content where (i) large amounts of
training data are unavailable, (ii) the objects to be recognized are complex,
(e.g., implicit entities and highly subjective content), and (iii) applications
need to use complementary or related data in multiple modalities/media. What
brings us to the cusp of rapid progress is our ability to (a) create relevant
and reliable knowledge and (b) carefully exploit knowledge to enhance ML/NLP
techniques. Using diverse examples, we seek to foretell unprecedented progress
in our ability for deeper understanding and exploitation of multimodal data and
continued incorporation of knowledge in learning techniques.Comment: Pre-print of the paper accepted at 2017 IEEE/WIC/ACM International
Conference on Web Intelligence (WI). arXiv admin note: substantial text
overlap with arXiv:1610.0770
Sentiment Recognition in Egocentric Photostreams
Lifelogging is a process of collecting rich source of information about daily
life of people. In this paper, we introduce the problem of sentiment analysis
in egocentric events focusing on the moments that compose the images recalling
positive, neutral or negative feelings to the observer. We propose a method for
the classification of the sentiments in egocentric pictures based on global and
semantic image features extracted by Convolutional Neural Networks. We carried
out experiments on an egocentric dataset, which we organized in 3 classes on
the basis of the sentiment that is recalled to the user (positive, negative or
neutral)
Business Ontology for Evaluating Corporate Social Responsibility
This paper presents a software solution that is developed to automatically classify companies by taking into account their level of social responsibility. The application is based on ontologies and on intelligent agents. In order to obtain the data needed to evaluate companies, we developed a web crawling module that analyzes the company’s website and the documents that are available online such as social responsibility report, mission statement, employment structure, etc. Based on a predefined CSR ontology, the web crawling module extracts the terms that are linked to corporate social responsibility. By taking into account the extracted qualitative data, an intelligent agent, previously trained on a set of companies, computes the qualitative values, which are then included in the classification model based on neural networks. The proposed ontology takes into consideration the guidelines proposed by the “ISO 26000 Standard for Social Responsibility”. Having this model, and being aware of the positive relationship between Corporate Social Responsibility and financial performance, an overall perspective on each company’s activity can be configured, this being useful not only to the company’s creditors, auditors, stockholders, but also to its consumers.corporate social responsibility, ISO 26000 Standard for Social Responsibility, ontology, web crawling, intelligent agent, corporate performance, POS tagging, opinion mining, sentiment analysis
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