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

Abstract

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.

On the usefulness of lexical and syntactic processing in polarity classification of Twitter messages

“…They then use the output labels of the unsupervised system to train a supervised classifier, improving both recall and F‐measure. Perea‐Ortega, Martín‐Valdivia, Ureña‐López, and Martínez‐Cámara () propose a different hybrid strategy: A voting model based on majority rule to conflate the results obtained by an SVM trained on word n‐grams with those obtained by a lexicon‐based approach. The system was used to classify a set of film reviews from a bilingual parallel corpus as positive or negative.…”

Integrating Spanish lexical resources by meta-classifiers for polarity classification

“…Martín-Valdivia et al [25] presented an experimental study of supervised and unsupervised approaches over a Spanish–English parallel corpus by integrating SWN in different ways over the translated English corpus. Perea-Ortega et al [26] carried out several experiments by combining both machine learning and semantic orientation approaches over the Opinion Corpus for Arabic (OCA) [19] and its parallel English version named EVOCA. They applied a voting system based on majority rule showing a slight improvement when both approaches were combined.…”

A knowledge‐based approach for polarity classification in Twitter

“…SentiWordNet returns from every synset a set of three scores representing the level of positivity, negativity, and objectivity (computed from the two previous scores). This resource has been used by the SA community because it provides a domain‐independent resource for obtaining certain information about the degree of sentiment charge of its concepts (Denecke, ; Ogawa, Ma, & Yoshikawa, ; Perea‐Ortega, Martín‐Valdivia, Ureña‐López, & Martínez‐Cámara, in press).…”