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

Basic tasks of sentiment analysis

Subjectivity detection is the task of identifying objective and subjective sentences. Objective sentences are those which do not exhibit any sentiment. So, it is desired for a sentiment analysis engine to find and separate the objective sentences for further analysis, e.g., polarity detection. In subjective sentences, opinions can often be expressed on one or multiple topics. Aspect extraction is a subtask of sentiment analysis that consists in identifying opinion targets in opinionated text, i.e., in detecting the specific aspects of a product or service the opinion holder is either praising or complaining about

Using Multi-Label Multi-Class Support Vector Machines with Semantic and Lexical Features for Aspect Category Detection

In contrast to the aspects, aspect categories are often coarser and don't always appear as terms in sentences. Besides, the typical way to element the types associated with part is generally grainier concerning factors and doesn't exist within verdicts. The primary intent of the study is to investigate the efficacy of Lexicon, linguistic, vector-based, and features correlated to semantics within the aspect of the responsibility built with the finding of aspect category detection ACD). Semantic and emotional data are captured via vector-based features. Further, it examines vector-based feature superiority issues within the compression of features of text-based characteristics. Study purposes to the linguistic efficacy with the Lexicon, linguistic, and semantic features, also vector-based dependent to the system. Also, the information led with vector-based features that capture the semantic with sentimental analysis characteristics. With the experimental outcomes, the performance efficacy with the vector-based features outperformed text-based features. The methodologies associated with deep learning have generated features within the vector orientation relevant to the word-based structures. Therefore, the proposed method achieved effectiveness with the determined constraints by applying the metrics of precision, recall, and F1 scores. Correlating with the performance of ABSA's state-of-the-art techniques, the proposed research process gained superior outcomes

Understanding user behavior aspects on emergency mobile applications during emergency communications using NLP and text mining techniques

Abstract. The use of mobile devices has been skyrocketing in our society. Users can access and share any type of information in a timely manner through these devices using different social media applications. This enabled users to increase their awareness of ongoing events such as election campaigns, sports updates, movie releases, disaster occurrences, and studies. The attractiveness, affordability, and two-way communication capabilities empowered these mobile devices that support various social media platforms to be central to emergency communication as well. This makes a mobile-based emergency application an attractive communication tool during emergencies. The emergence of mobile-based emergency communication has intrigued us to learn about the user behavior related to the usage of these applications. Our study was mainly conducted on emergency apps in Nordic countries such as Finland, Sweden, and Norway. To understand the user objects regarding the usage of emergency mobile applications we leveraged various Natural Language Processing and Text Mining techniques. VADER sentiment tool was used to predict and track users’ review polarity of a particular application over time. Lately, to identify factors that affect users’ sentiments, we employed topic modeling techniques such as the Latent Dirichlet Allocation (LDA) model. This model identifies various themes discussed in the user reviews and the result of each theme will be represented by the weighted sum of words in the corpus. Even though LDA succeeds in highlighting the user-related factors, it fails to identify the aspects of the user, and the topic definition from the LDA model is vague. Hence we leveraged Aspect Based Sentiment Analysis (ABSA) methods to extract the user aspects from the user reviews. To perform this task we consider fine-tuning DeBERTa (a variant of the BERT model). BERT is a Bidirectional Encoder Representation of transformer architecture which allows the model to learn the context in the text. Following this, we performed a sentence pair sentiment classification task using different variants of BERT. Later, we dwell on different sentiments to highlight the factors and the categories that impact user behavior most by leveraging the Empath categorization technique. Finally, we construct a word association by considering different Ontological vocabularies related to mobile applications and emergency response and management systems. The insights from the study can be used to identify the user aspect terms, predict the sentiment of the aspect term in the review provided, and find how the aspect term impacts the user perspective on the usage of mobile emergency applications

Multilingual opinion mining

170 p.Cada día se genera gran cantidad de texto en diferentes medios online. Gran parte de ese texto contiene opiniones acerca de multitud de entidades, productos, servicios, etc. Dada la creciente necesidad de disponer de medios automatizados para analizar, procesar y explotar esa información, las técnicas de análisis de sentimiento han recibido gran cantidad de atención por parte de la industria y la comunidad científica durante la última década y media. No obstante, muchas de las técnicas empleadas suelen requerir de entrenamiento supervisado utilizando para ello ejemplos anotados manualmente, u otros recursos lingüísticos relacionados con un idioma o dominio de aplicación específicos. Esto limita la aplicación de este tipo de técnicas, ya que dicho recursos y ejemplos anotados no son sencillos de obtener. En esta tesis se explora una serie de métodos para realizar diversos análisis automáticos de texto en el marco del análisis de sentimiento, incluyendo la obtención automática de términos de un dominio, palabras que expresan opinión, polaridad del sentimiento de dichas palabras (positivas o negativas), etc. Finalmente se propone y se evalúa un método que combina representación continua de palabras (continuous word embeddings) y topic-modelling inspirado en la técnica de Latent Dirichlet Allocation (LDA), para obtener un sistema de análisis de sentimiento basado en aspectos (ABSA), que sólo necesita unas pocas palabras semilla para procesar textos de un idioma o dominio determinados. De este modo, la adaptación a otro idioma o dominio se reduce a la traducción de las palabras semilla correspondientes