Towards Syntactic Iberian Polarity Classification

Lexicon-based methods using syntactic rules for polarity classification rely on parsers that are dependent on the language and on treebank guidelines. Thus, rules are also dependent and require adaptation, especially in multilingual scenarios. We tackle this challenge in the context of the Iberian Peninsula, releasing the first symbolic syntax-based Iberian system with rules shared across five official languages: Basque, Catalan, Galician, Portuguese and Spanish. The model is made available.Comment: 7 pages, 5 tables. Contribution to the 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis (WASSA-2017) at EMNLP 201

Semantic Sentiment Analysis of Twitter Data

Internet and the proliferation of smart mobile devices have changed the way information is created, shared, and spreads, e.g., microblogs such as Twitter, weblogs such as LiveJournal, social networks such as Facebook, and instant messengers such as Skype and WhatsApp are now commonly used to share thoughts and opinions about anything in the surrounding world. This has resulted in the proliferation of social media content, thus creating new opportunities to study public opinion at a scale that was never possible before. Naturally, this abundance of data has quickly attracted business and research interest from various fields including marketing, political science, and social studies, among many others, which are interested in questions like these: Do people like the new Apple Watch? Do Americans support ObamaCare? How do Scottish feel about the Brexit? Answering these questions requires studying the sentiment of opinions people express in social media, which has given rise to the fast growth of the field of sentiment analysis in social media, with Twitter being especially popular for research due to its scale, representativeness, variety of topics discussed, as well as ease of public access to its messages. Here we present an overview of work on sentiment analysis on Twitter.Comment: Microblog sentiment analysis; Twitter opinion mining; In the Encyclopedia on Social Network Analysis and Mining (ESNAM), Second edition. 201

Comprehensive Review of Opinion Summarization

The abundance of opinions on the web has kindled the study of opinion summarization over the last few years. People have introduced various techniques and paradigms to solving this special task. This survey attempts to systematically investigate the different techniques and approaches used in opinion summarization. We provide a multi-perspective classification of the approaches used and highlight some of the key weaknesses of these approaches. This survey also covers evaluation techniques and data sets used in studying the opinion summarization problem. Finally, we provide insights into some of the challenges that are left to be addressed as this will help set the trend for future research in this area.unpublishednot peer reviewe

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

COPOS: Corpus de Opiniones de Pacientes en Español. Aplicación de Técnicas de Análisis de Sentimientos

Every day more users are interested in the opinion that other patients have about a physician or about health topics in general. According to a study in 2015, 62% of Spanish people access the Internet in order to be informed about topics related to health. This paper is focused on Spanish Sentiment Analysis in the medical domain. Although Sentiment Analysis has been studied for different domains, health issues have hardly been examined in Opinion Mining and even less with Spanish comments or opinions. Thus we have generated a corpus by crawling the website Masquemedicos with Spanish opinions about medical entities written by patients. We present this new resource, called COPOS (Corpus Of Patient Opinions in Spanish). To the best of our knowledge, this is the first attempt to deal with Spanish opinions written by patients about medical attention. In order to demonstrate the validity of the corpus presented, we have also carried out different experiments with the main methodologies applied in polarity classification (Semantic Orientation and Machine Learning). The results obtained encourage us to continue analysing and researching Opinion Mining in the medical domain.Cada día son más los usuarios interesados en la opinión que otros pacientes tienen sobre un médico o sobre temas de salud en general. De acuerdo con un estudio de 2015, el 62% de la población española consulta información en Internet acerca de temas relacionados con la salud. Este trabajo está centrado en el Análisis de Sentimientos en español aplicado al dominio médico. Aunque el Análisis de Sentimientos ha sido estudiado en diferentes dominios, el dominio de la salud apenas ha sido investigado, especialmente en opiniones escritas en español. Por ello, hemos generado un corpus en español con opiniones de pacientes sobre médicos a partir de la extracción de las mismas del portal web Masquemedicos. Este corpus ha sido denominado COPOS (Corpus Of Patient Opinions in Spanish - Corpus de Opiniones de Pacientes en Español). Hasta donde sabemos, es la primera vez que se intenta trabajar con opiniones en español sobre atención médica escritas por pacientes. Para demostrar la validez de este recurso, hemos realizado diferentes experimentos con las principales metodologías aplicadas en la tarea de clasificación de polaridad (Orientación Semántica y Aprendizaje Automático). Los resultados obtenidos nos animan a seguir investigando en el Análisis de Sentimientos en este dominio.This work has been partially supported by a grant from the Fondo Europeo de Desarrollo Regional (FEDER), REDES project (TIN2015-65136-C2-1-R) from the Spanish Government and by a Grant from the Ministerio de Educación Cultura y Deporte (MECD - scholarship FPU014/00983)

SentiTur: Building Linguistic Resources for Aspect-Based Sentiment Analysis in the Tourism Sector

The use of linguistic resources beyond the scope of language studies, i.e. commercial purposes, has become commonplace since the availability of massive amounts of data and the development of tools to process them. An interesting focus on these materials is provided by Sentiment Analysis (SA) tools and methodologies, which attempt to identify the polarity or semantic orientation of a text, i.e., its positive, negative, or neutral value. Two main approaches have been made in this sense, one based on complex machine-learning algorithms and the other relying principally on lexical knowledge (Taboada et al., 2011). Lingmotif is an example of lexicon-based SA tool offering polarity classification and other related metrics, together with an analysis of the target segments evaluated (Moreno-Ortiz, 2017). Sentiment has been shown to be domain-specific to a large extent (Choi & Cardie, 2008) and it is therefore necessary to study and describe how sentiment is expressed not only in general language, but also in specialized domains. The availability of annotated, domain-specific corpora could greatly enhance the capacity of SA tools. Furthermore, the demand for a more fine-grained approach requires the identification of specific domain terminology, allowing the recognition of target terms associated with the polarity (Liu, 2012). Most available SA corpora are annotated at the document level, which allows systems to be trained to return the overall orientation of the text. However, more detail is necessary: what aspects exactly are being praised or criticized? This type SA is known as Aspect-Based Sentiment Analysis (ABSA), and attempts to extract more fined-grained knowledge. ABSA has attracted the attention of recent SemEval shared-tasks (Pontiki et al., 2015)