Semantic Orientation Approach Research Articles

A New Ontology-Based Method for Arabic Sentiment Analysis

Arabic sentiment analysis is a process that aims to extract the subjective opinions of different users about different subjects since these opinions and sentiments are used to recognize their perspectives and judgments in a particular domain. Few research studies addressed semantic-oriented approaches for Arabic sentiment analysis based on domain ontologies and features’ importance. In this paper, we built a semantic orientation approach for calculating overall polarity from the Arabic subjective texts based on built domain ontology and the available sentiment lexicon. We used the ontology concepts to extract and weight the semantic domain features by considering their levels in the ontology tree and their frequencies in the dataset to compute the overall polarity of a given textual review based on the importance of each domain feature. For evaluation, an Arabic dataset from the hotels’ domain was selected to build the domain ontology and to test the proposed approach. The overall accuracy and f-measure reach 79.20% and 78.75%, respectively. Results showed that the approach outperformed the other semantic orientation approaches, and it is an appealing approach to be used for Arabic sentiment analysis.

Tuning of Customer Relationship Management (CRM) via Customer Experience Management (CEM) using Sentiment Analysis on Aspects Level

This study proposes a framework that combines a supervised machine learning and a semantic orientation approach to tune Customer Relationship Management (CRM) via Customer Experience Management (CEM). The framework extracts data from social media first and then integrates CRM and CEM by tuning and optimising CRM to reflect the needs and expectations of users on social media. In other words, in order to reduce the gap between the users’ predicted opinions in CRM and their opinions on social media, the existing data from CEM will be applied to determine the similar behavioural patterns of customers towards similar outcomes within CRM. CRM data and extracted data from social media will be consolidated by the unsupervised data mining method (association). The framework will lead to a quantitative approach to uncover relationships between the extracted data from social media and the CRM data. The results show that changing some aspects of the e-learning criteria that were required by students in their social media posts can help to enhance the classification accuracy in the learning management system (LMS) data and to understand more students’ studying statuses. Furthermore, the results show matching between students’ opinions in CRM and CEM, especially in the negative and neutral classes.

Utility of Corpus based Approach in the Recognition of Opinionated Text

Objectives: The proposed work focuses on mining opinion word catalogue by using corpus based approach. The motive is to use different parts of speech to improve the classification of the sentiments. Methods/Statistical Analysis: The methodology involved in the proposed work incorporates both the sentiment orientation approach and machine learning approach. The various features like content – specific, content-free and other sentiment features have been used to classify the sentiments. The previous works in the field involved only some specific parts of speech, which have been replaced by the usage of nouns, adjectives, verbs and adverbs. In this approach an algorithm for calculation of sentiment feature has been proposed. Findings: The algorithm proposed in this work is more efficient in comparison to other existing work. In this work, since we have developed a corpus based approach amalgamating both the machine learning and semantic orientation approaches into a common skeleton, it improvises the classification method. Our projected method also incorporates the content-specific and content free features involved in the existing approaches. It also utilizes the infrequent and sentiment features in the semantic orientation approach. The proposed technique can be classified into three main modules: Acquiring of data, generation of features, followed by classification and evaluation. Application/ Improvements: The researches to be done in future can deal with other feature generation methods. Moreover the method can also be improved by making the modifications so that the feature classification can be done on quite large data sets. The method can further be implemented for multilingual languages to build a multilingual sentiment-based lexicon.. Keywords: Corpus–based Approach, Opinionated Text, Sentiment Analysis, Sentiment Classification, Sentiment Features

A Spanish semantic orientation approach to domain adaptation for polarity classification

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.