A polarity analysis framework for Twitter messages
| dc.contributor.author | Lima A.C.E.S. | |
| dc.contributor.author | De Castro L.N. | |
| dc.contributor.author | Corchado J.M. | |
| dc.date.accessioned | 2024-03-13T00:56:05Z | |
| dc.date.available | 2024-03-13T00:56:05Z | |
| dc.date.issued | 2015 | |
| dc.description.abstract | © 2015 Elsevier Inc. All rights reserved.Social media, such as Twitter and Facebook, allow the creation, sharing and exchange of information among people, companies and brands. This information can be used for several purposes, such as to understand consumers and their preferences. In this direction, the sentiment analysis can be used as a feedback mechanism. This analysis corresponds to classifying a text according to the sentiment that the writer intended to transmit. A basic sentiment classifier determines the sentiment polarity (negative, neutral or positive) of a given text at the document, sentence, or feature/aspect level. Advanced types may consider other elements like the emotional state (e.g. angry, sad, happy), affective states (e.g. pleasure and pain), motivational states (e.g. hunger and curiosity), temperaments, among others. In general, there are two main approaches to attribute sentiment to tweets: based on knowledge; or based on machine learning algorithms. In the latter case, the learning algorithm requires a pre-classified data sample to determine the class of new data. Typically, the sample is pre-classified manually, making the process time consuming and reducing its real time applicability for big data. This paper proposes a polarity analysis framework for Twitter messages, which combines both approaches and an automatic contextual module. To assess the performance of the proposed framework, four text datasets from the literature are used. Five different types of classifiers were considered: Naïve Bayes (NB); Support Vector Machines (SVM); Decision Trees (J48); and Nearest Neighbors (KNN). The results show that the proposal is a suitable framework to automate the whole polarity analysis process, providing high accuracy levels and low false positive rates. | |
| dc.description.firstpage | 756 | |
| dc.description.lastpage | 767 | |
| dc.description.volume | 270 | |
| dc.identifier.doi | 10.1016/j.amc.2015.08.059 | |
| dc.identifier.issn | 0096-3003 | |
| dc.identifier.uri | https://dspace.mackenzie.br/handle/10899/36135 | |
| dc.relation.ispartof | Applied Mathematics and Computation | |
| dc.rights | Acesso Restrito | |
| dc.subject.otherlanguage | Classification task | |
| dc.subject.otherlanguage | Machine learning | |
| dc.subject.otherlanguage | Sentiment analysis | |
| dc.subject.otherlanguage | Social media | |
| dc.subject.otherlanguage | Text mining | |
| dc.subject.otherlanguage | ||
| dc.title | A polarity analysis framework for Twitter messages | |
| dc.type | Artigo | |
| local.scopus.citations | 110 | |
| local.scopus.eid | 2-s2.0-84940981567 | |
| local.scopus.subject | Classification tasks | |
| local.scopus.subject | Sentiment analysis | |
| local.scopus.subject | Social media | |
| local.scopus.subject | Text mining | |
| local.scopus.subject | ||
| local.scopus.updated | 2024-05-01 | |
| local.scopus.url | https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84940981567&origin=inward |