M. Teresa Martín-Valdivia

OCA: Opinion corpus for Arabic

Sentiment analysis is a challenging new task related to text mining and natural language processing. Although there are, at present, several studies related to this theme, most of these focus mainly on English texts. The resources available for opinion mining (OM) in other languages are still limited. In this article, we present a new Arabic corpus for the OM task that has been made available to the scientific community for research purposes. The corpus contains 500 movie reviews collected from different web pages and blogs in Arabic, 250 of them considered as positive reviews, and the other 250 as negative opinions. Furthermore, different experiments have been carried out on this corpus, using machine learning algorithms such as support vector machines and Nave Bayes. The results obtained are very promising and we are encouraged to continue this line of research.

Sentiment analysis in Twitter

In recent years, the interest among the research community in sentiment analysis (SA) has grown exponentially. It is only necessary to see the number of scientific publications and forums or related conferences to understand that this is a field with great prospects for the future. On the other hand, the Twitter boom has boosted investigation in this area due fundamentally to its potential applications in areas such as business or government intelligence, recommender systems, graphical interfaces and virtual assistance. However, to fully understand this issue, a profound revision of the state of the art is first necessary. It is for this reason that this paper aims to represent a starting point for those investigations concerned with the latest references to Twitter in SA.

Ranked WordNet graph for Sentiment Polarity Classification in Twitter

This paper presents a novel approach to Sentiment Polarity Classification in Twitter posts, by extracting a vector of weighted nodes from the graph of WordNet. These weights are used in SentiWordNet to compute a final estimation of the polarity. Therefore, the method proposes a non-supervised solution that is domain-independent. The evaluation of a generated corpus of tweets shows that this technique is promising.

Opinion classification techniques applied to a Spanish corpus

Sentiment analysis is a new challenging task related to Text Mining and Natural Language Processing. Although there are some current works, most of them only focus on English texts. Web pages, information and opinions on the Internet are increasing every day, and English is not the only language used to write them. Other languages like Spanish are increasingly present so we have carried out some experiments over a Spanish film reviews corpus. In this paper we present several experiments using five classification algorithms (SVM, Nave Bayes, BBR, KNN, C4.5). The results obtained are very promising and encourage us to continue investigating in this line.

A knowledge-based approach for polarity classification in Twitter

Until now, most of the methods published for polarity classification in Twitter have used a supervised approach. The differences between them are only the features selected and the method used for weighting them. In this article, we present an unsupervised method for polarity classification in Twitter. The method is based on the expansion of the concepts expressed in the tweets through the application of PageRank to WordNet. In addition, we integrate SentiWordNet to compute the final value of polarity. The synsets values are weighted with the PageRank scores obtained in the previous random walk process over WordNet. The results obtained show that disambiguation and expansion are good strategies for improving overall performance.

A Spanish semantic orientation approach to domain adaptation for polarity classification

A lexicon-based domain adaptation method is proposed.Several domain polar lexicons were compiled following a corpus-based approach.The new resources are assessed over a Spanish corpus.The promising results encourage us to follow improving this domain adaptation method. One of the problems of opinion mining is the domain adaptation of the sentiment classifiers. There are several approaches to tackling this problem. One of these is the integration of a list of opinion bearing words for the specific domain. This paper presents the generation of several resources for domain adaptation to polarity detection. On the other hand, the lack of resources in languages different from English has orientated our work towards developing sentiment lexicons for polarity classifiers in Spanish. The results show the validity of the new sentiment lexicons, which can be used as part of a polarity classifier.

Improving polarity classification of bilingual parallel corpora combining machine learning and semantic orientation approaches

Polarity classification is one of the main tasks related to the opinion mining and sentiment analysis fields. The aim of this task is to classify opinions as positive or negative. There are two main approaches to carrying out polarity classification: machine learning and semantic orientation based on the integration of knowledge resources. In this study, we propose to combine both approaches using a voting system based on the majority rule. In this way, we attempt to improve the polarity classification of two parallel corpora such as the opinion corpus for Arabic (OCA) and the English version of the OCA (EVOCA). Several experiments have been performed to check the feasibility of the proposed method. The results show that the experiment that took into account both approaches in the voting system obtained the best performance. Moreover, it is also shown that the proposed method slightly improves the best results obtained using machine learning approaches solely over the OCA and the EVOCA separately. Therefore, we can conclude that the approach proposed here might be considered a good strategy for polarity detection when we work with bilingual parallel corpora.

Polarity classification for Spanish tweets using the COST corpus

It was not until 2010 when businesses, politicians and people in general began to realize the potential of Twitter in Spain. This fact has awoken research interest in the extraction of knowledge from Twitter. This paper aims to fill the gap of the lack of resources for Twitter sentiment analysis in Spanish by performing a study of different features and machine learning algorithms for classifying the polarity of Twitter posts. The result is a new corpus of Spanish tweets called COST, and we have carried out a wide-ranging experiment in which different machine learning algorithms have been used. Furthermore, we have tested the influence of using different weighting schemes for unigrams, the influence of eliminating stop-words and the application of a stemmer process.

LVQ for text categorization using a multilingual linguistic resource

Abstract Neural learning has been used with effectiveness in natural language processing tasks. Particularly, the Widrow–Hoff and the Kivinen–Warmuth exponentiated gradient (based on neural learning rules) algorithms have been used in text categorization, improving the results obtained by the well-known Rocchios algorithm. The high performance of competitive learning algorithms, recently applied to solve information retrieval problems, leads us to use them in the specific text categorization tasks. This paper presents a multilingual categorization system based on neural learning, using the polyglot Bible as training collection, both in Spanish and English. The method we suggest is based on using the LVQ algorithm to build a classifier that learns the training multilingual collection. We have performed experiments with the four algorithm which show that the ideas we describe are promising and are worth further investigation.

Combining resources to improve unsupervised sentiment analysis at aspect-level

Every day more companies are interested in users’ opinions about their products or services. Also, every day there are more users that search for reviews on the web before purchasing a product. These users and companies are not satisfied with knowing the overall sentiment of a product, they want a finer knowledge of users’ opinions. Owing to this fact, more and more researchers are working on sentiment analysis at aspect-level. This paper describes an unsupervised approach for aspect-based sentiment analysis, which aims to identify the aspects of given target entities and the sentiment expressed for each aspect. We have evaluated several tasks, although perhaps the major novelty is in the classification of the aspects. We employ a lexicon-based method combining different linguistic resources and we conclude that the combination of several classifiers improves the classification significantly. In addition, a comparison with a supervised system is performed in order to determine the strengths and weakness of each of them.