TASS 2015 – La evolución de los sistemas de análisis de opiniones para español

El análisis de opiniones en microblogging sigue siendo una tarea de actualidad, que permite conocer la orientación de las opiniones que minuto tras minuto se publican en medios sociales en Internet. TASS es un taller de participación que tiene como finalidad promover la investigación y desarrollo de nuevos algoritmos, recursos y técnicas aplicado al análisis de opiniones en español. En este artículo se describe la cuarta edición de TASS, resumiendo las principales aportaciones de los sistemas presentados, analizando los resultados y mostrando la evolución de los mismos. Además de analizar brevemente los sistemas que se presentaron, se presenta un nuevo corpus de tweets etiquetados en el dominio político, que se desarrolló para la tarea de Análisis de Opiniones a nivel de Aspecto.Sentiment Analysis in microblogging continues to be a trendy task, which allows to understand the polarity of the opinions published in social media. TASS is a workshop whose goal is to boost the research on Sentiment Analysis in Spanish. In this paper we describe the fourth edition of TASS, showing a summary of the systems, analyzing the results to check their evolution. In addition to a brief description of the participant systems, a new corpus of tweets is presented, compiled for the Sentiment Analysis at Aspect Level task.This work has been partially supported by a grant from the Fondo Europeo de Desarrollo Regional (FEDER), REDES project (TIN2015-65136-C2-1-R) and Ciudad2020 (INNPRONTA IPT-20111006) from the Spanish Government

Unraveling e-WOM patterns using text mining and sentiment analysis

Electronic word-of-mouth (e-WOM) is a very important way for firms to measure the pulse of its online reputation. Today, consumers use e-WOM as a way to interact with companies and share not only their satisfaction with the experience, but also their discontent. E-WOM is even a good way for companies to co-create better experiences that meet consumer needs. However, not many companies are using such unstructured information as a valuable resource to help in decision making. First, because e-WOM is mainly textual information that needs special data treatment and second, because it is spread in many different platforms and occurs in near-real-time, which makes it hard to handle. The current chapter revises the main methodologies used successfully to unravel hidden patterns in e-WOM in order to help decision makers to use such information to better align their companies with the consumer’s needs.info:eu-repo/semantics/acceptedVersio

Aplicação da Análise de Sentimento para Avaliar Mensagens Significativas em um Ambiente Colaborativo: um estudo de caso no ambiente Collabora

Este artigo apresenta a aplicação da análise de sentimentos em uma base de dados que contém mensagens em chats de atividades realizadas por alunos no objeto virtual de aprendizagem colaborativa Collabora. A implementação das etapas para avaliar as mensagens significativas foi em linguagem Python com o uso de duas ferramentas de análise de sentimentos (NTLK e spaCy). Os dados processados pelas ferramentas foram comparados entre eles e aplicados ao cálculo de colaboração, reformulado do trabalho de Ishikawa (2018), a fim de criar uma métrica a partir das mensagens com sentimentos. O resultado permitiu verificar que o sentimento total das mensagens do grupo ou do aluno, podem refletir no comprometimento do aluno com a disciplina ou exercício proposto

Social media and public administration : social sentiment analysis about the performance of the Brazilian Federal Government

Este estudo procurou identificar como a análise de sentimento, baseada em textos extraídos de mídias sociais, pode ser um instrumento de mensuração da opinião pública sobre a atuação do governo, de forma a contribuir para a avaliação da administração pública. Trata-se de um estudo aplicado, interdisciplinar, exploratório, qualitativo e quantitativo. Foram revisadas as principais formulações teóricas e conceituais acerca do tema e realizadas demonstrações práticas, utilizando-se uma ferramenta de mineração de opinião que proporcionou precisão satisfatória no processamento de dados. Para fins de demonstração, foram selecionados temas que motivaram a realização da onda de protestos que envolveu milhões de pessoas no Brasil em junho de 2013. Foram coletadas, processadas e analisadas, aproximadamente, 130 mil mensagens postadas no Facebook e no Twitter sobre esses temas em dois períodos distintos. Por meio dessa investigação, observou-se que a análise de sentimento pode revelar a opinião polarizada dos cidadãos quanto à atuação do governo.This study sought to identify as sentiment analysis, based on texts taken social media can be a measuring instrument of public opinion on the government’s performance in order to contribute to the evaluation of public administration. This is an applied study, interdisciplinary, exploratory, qualitative and quantitative. The main theoretical and conceptual formulations on the subject were reviewed and conducted practical demonstrations using an opinion mining tool which provided satisfactory precision in data processing. For demonstration purposes, themes were selected that motivated the wave of protests involving millions of people in Brazil in June 2013. They were collected, processed and analyzed approximately 130,000 messages posted on Facebook and Twitter on these topics in two distinct periods. Through this analysis, it was observed that the sentiment analysis can reveal the polarized opinions of citizens about the government’s performance

Support Vector Machine Berbasis Feature Selection Untuk Sentiment Analysis Kepuasan Pelanggan Terhadap Pelayanan Warung dan Restoran Kuliner Kota Tegal

Abstrak   Setiap pelanggan pasti menginginkan sebuah pendukung keputusan dalam menentukan pilihan ketika akan mengunjungi sebuah tempat makan atau kuliner yang sesuai dengan keinginan salah satu contohnya yaitu di Kota Tegal. Sentiment analysis digunakan untuk memberikan sebuah solusi terkait dengan permasalahan tersebut, dengan menereapkan model algoritma Support Vector Machine (SVM). Tujuan dari penelitian ini adalah mengoptimalisasi model yang dihasilkan dengan diterapkannya feature selection menggunakan algoritma Informatioan Gain (IG) dan Chi Square pada hasil model terbaik yang dihasilkan oleh SVM pada klasifikasi tingkat kepuasan pelanggan terhadap warung dan restoran kuliner di Kota Tegal sehingga terjadi peningkatan akurasi dari model yang dihasilkan. Hasil penelitian menunjukan bahwa tingkat akurasi terbaik dihasilkan oleh model SVM-IG dengan tingkat akurasi terbaik sebesar 72,45% mengalami peningkatan sekitar 3,08% yang awalnya 69.36%. Selisih rata-rata yang dihasilkan setelah dilakukannya optimasi SVM dengan feature selection adalah 2,51% kenaikan tingkat akurasinya. Berdasarkan hasil penelitian bahwa feature selection dengan menggunakan Information Gain (IG) (SVM-IG) memiliki tingkat akurasi lebih baik apabila dibandingkan SVM dan Chi Squared (SVM-CS) sehingga dengan demikian model yang diusulkan dapat meningkatkan tingkat akurasi yang dihasilkan oleh SVM menjadi lebih baik. Abstract   The Customer needs to get a decision support in determining a choice when they’re visit a culinary restaurant accordance to their wishes especially at Tegal City. Sentiment analysis is used to provide a solution related to this problem by applying the Support Vector Machine (SVM) algorithm model. The purpose of this research is to optimize the generated model by applying feature selection using Informatioan Gain (IG) and Chi Square algorithm on the best model produced by SVM on the classification of customer satisfaction level based on culinary restaurants at Tegal City so that there is an increasing accuracy from the model. The results showed that the best accuracy level produced by the SVM-IG model with the best accuracy of 72.45% experienced an increase of about 3.08% which was initially 69.36%. The difference average produced after SVM optimization with feature selection is 2.51% increase in accuracy. Based on the results of the research, the feature selection using Information Gain (SVM-IG) has a better accuracy rate than SVM and Chi Squared (SVM-CS) so that the proposed model can improve the accuracy of SVM better