Extração de Métricas e Análise de Sentimentos em Comentários Web no Domínio de Hotéis
Resumo
O significativo crescimento da Web tem a tornado uma rica fonte para a avaliação da opinião pública sobre uma entidade específica. Consequentemente, o número de opiniões disponíveis torna impossível uma tomada de decisão se for necessário ler e analisar todas as opiniões. Este trabalho apresenta um protótipo de uma aplicação Web que a partir de um comentário seja retornado o sentimento (positivo, negativo ou neutro), suas características e outras métricas de análise utilizando técnicas de Processamento de Linguagem Natural e Análise de Sentimentos. Experimentos mostram eficácia nos resultados de precisão de comentários com polaridade negativa e cobertura de comentários positivos em 84,93% e 94,33% respectivamente.
Palavras-chave:
Análise de Sentimentos, Mineração de Opiniões, Processamento de Língua Natural
Referências
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Hasany, N. and Selamat, M. (2011). Answering user queries from hotel ontology for decision making. pages 123–127.
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Hu, M. and Liu, B. (2004). Mining opinion features in customer reviews. In Proceedings of the 19th National Conference on Artifical Intelligence, pages 755–760.
Indhuja, K. and Reghu, R. P. C. (2014). Fuzzy logic based sentiment analysis of product review documents. In Computational Systems and Communications (ICCSC), 2014 First International Conference on, pages 18–22.
Khan, K., Baharudin, B., Khan, A., and e Malik, F. (2009). Mining opinion from text documents: A survey. In 3rd IEEE International Conference on Digital Ecosystems and Technologies, pages 217–222.
Kim, S.-M., Pantel, P., Chklovski, T., and Pennacchiotti, M. (2006). Automatically assessing review helpfulness. In Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing, pages 423–430.
Klavans, J. and Kan, M.-Y. (1998). Role of verbs in document analysis. In Proceedings of the 36th Annual Meeting of the Association for Computational Linguistics and 17th International Conference on Computational Linguistics - Volume 1, ACL ’98, pages 680–686.
Laryea, B., Choi, C.-H., Jung, I.-S., Lee, K.-H., and Cho, W.-S. (2015). Web application for sentiment analysis using supervised machine learning. International Journal of Software Engineering and its Applications, 9(1):191–200.
Li, M., Huang, L., Tan, C.-h., and Wei, K. K. (2013). Helpfulness of online product reviews as seen by consumers : source and content features. International journal of electronic commerce : IJEC, 17(4):101–136.
Li, Z., Zhang, M., Ma, S., Zhou, B., and Sun, Y. (2009). Information Retrieval Technology: 5th Asia Information Retrieval Symposium, AIRS 2009, Sapporo, Japan, October 21-23, 2009. Proceedings, chapter Automatic Extraction for Product Feature Words from Comments on the Web, pages 112–123. Springer Berlin Heidelberg.
Liu, B. (2010). Handbook of natural language processing, chapter Sentiment analysis and subjectivity. Chapman and Hall/CRC.
Liu, B. (2012). Sentiment analysis and opinion mining. Morgan and Claypool.
Liu, H. and Singh, P. (2004). Conceptnet — a practical commonsense reasoning tool-kit. BT Technology Journal, 22(4):211–226.
Mullen, T. and Collier, N. (2004). Sentiment analysis using support vector machines with diverse information sources. In Proceedings of EMNLP 2004, pages 412–418.
Santos, R. L. S., Rabelo, R. A. L., and Moura, R. S. (2016). An experimental study based on fuzzy systems and artificial neural networks to estimate the importance of reviews about product and services. In 2016 International Joint Conference on Neural Networks (IJCNN 2016), page ”to appear”.
Sarlan, A., Nadam, C., and Basri, S. (2014). Twitter sentiment analysis. pages 212–216.
Seerat, B. and Azam, F. (2012). Opinion mining: Issues and challenges (a survey). International Journal of Computer Applications, 49(9):42–51.
Sharma, A. and Dey, S. (2012a). An artificial neural network based approach for sentiment analysis of opinionated text. In Proceedings of the 2012 ACM Research in Applied Computation Symposium, pages 37–42.
Sharma, A. and Dey, S. (2012b). A document-level sentiment analysis approach using artificial neural network and sentiment lexicons. SIGAPP Appl. Comput. Rev., 12(4):67–75.
Silva, M. J., Carvalho, P., and Sarmento, L. (2012). Building a sentiment lexicon for social judgement mining. In Proceedings of the 10th International Conference on Computational Processing of the Portuguese Language, pages 218–228.
Tan, C., Lee, L., Tang, J., Jiang, L., Zhou, M., and Li, P. (2011). User-level sentiment analysis incorporating social networks. In Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 1397–1405.
Torruella, J. and Capsada, R. (2013). Lexical statistics and tipological structures: A measure of lexical richness. Procedia - Social and Behavioral Sciences, 95:447 – 454.
Vinodhini, G. and Chandrasekaran, R. (2012). Sentiment analysis and opinion mining: a survey. International Journal of Advanced Research in Computer Science and Software Engneering, 2(6).
Yannis, P. and Evanthia, K. (2012). An ontology-based question answering system exploiting search engines’ results. pages 426–429.
Bird, S., Klein, E., and Loper, E. (2012). Natural Language Processing with Python. O‘Reilly.
De Freitas, L. and Vieira, R. (2013). Ontology-based feature level opinion mining for portuguese reviews. pages 367–370.
de Sousa, R. F., Rabelo, R. A. L., and Moura, R. S. (2015). A fuzzy system-based approach to estimate the importance of online customer reviews. In 2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), pages 1–8.
Dias-Da-Silva, B. C. (2010). Brazilian portuguese wordnet: A computational linguistic exercise of encoding bilingual relational lexicons. International Journal of Computational Linguistics and Applications, 1(1-2):137–150.
Dragoni, M., Tettamanzi, A. G. B., and Costa Pereira, C. (2014). Semantic Web Evaluation Challenge: SemWebEval 2014 at ESWC 2014, Anissaras, Crete, Greece, May 25-29, 2014, Revised Selected Papers, chapter A Fuzzy System for Concept-Level Sentiment Analysis, pages 21–27. Springer International Publishing.
Farina, J., Mazuran, M., and Quintarelli, E. (2014). New Trends in Databases and Information Systems: 17th East European Conference on Advances in Databases and Information Systems, chapter Extraction, Sentiment Analysis and Visualization of Massive Public Messages. Springer International Publishing.
Hasany, N. and Selamat, M. (2011). Answering user queries from hotel ontology for decision making. pages 123–127.
Htay, S. S. and Lynn, K. T. (2013). Extracting product features and opinion words using pattern knowledge in customer reviews. The Scientific World Journal, 2013(394758).
Hu, M. and Liu, B. (2004). Mining opinion features in customer reviews. In Proceedings of the 19th National Conference on Artifical Intelligence, pages 755–760.
Indhuja, K. and Reghu, R. P. C. (2014). Fuzzy logic based sentiment analysis of product review documents. In Computational Systems and Communications (ICCSC), 2014 First International Conference on, pages 18–22.
Khan, K., Baharudin, B., Khan, A., and e Malik, F. (2009). Mining opinion from text documents: A survey. In 3rd IEEE International Conference on Digital Ecosystems and Technologies, pages 217–222.
Kim, S.-M., Pantel, P., Chklovski, T., and Pennacchiotti, M. (2006). Automatically assessing review helpfulness. In Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing, pages 423–430.
Klavans, J. and Kan, M.-Y. (1998). Role of verbs in document analysis. In Proceedings of the 36th Annual Meeting of the Association for Computational Linguistics and 17th International Conference on Computational Linguistics - Volume 1, ACL ’98, pages 680–686.
Laryea, B., Choi, C.-H., Jung, I.-S., Lee, K.-H., and Cho, W.-S. (2015). Web application for sentiment analysis using supervised machine learning. International Journal of Software Engineering and its Applications, 9(1):191–200.
Li, M., Huang, L., Tan, C.-h., and Wei, K. K. (2013). Helpfulness of online product reviews as seen by consumers : source and content features. International journal of electronic commerce : IJEC, 17(4):101–136.
Li, Z., Zhang, M., Ma, S., Zhou, B., and Sun, Y. (2009). Information Retrieval Technology: 5th Asia Information Retrieval Symposium, AIRS 2009, Sapporo, Japan, October 21-23, 2009. Proceedings, chapter Automatic Extraction for Product Feature Words from Comments on the Web, pages 112–123. Springer Berlin Heidelberg.
Liu, B. (2010). Handbook of natural language processing, chapter Sentiment analysis and subjectivity. Chapman and Hall/CRC.
Liu, B. (2012). Sentiment analysis and opinion mining. Morgan and Claypool.
Liu, H. and Singh, P. (2004). Conceptnet — a practical commonsense reasoning tool-kit. BT Technology Journal, 22(4):211–226.
Mullen, T. and Collier, N. (2004). Sentiment analysis using support vector machines with diverse information sources. In Proceedings of EMNLP 2004, pages 412–418.
Santos, R. L. S., Rabelo, R. A. L., and Moura, R. S. (2016). An experimental study based on fuzzy systems and artificial neural networks to estimate the importance of reviews about product and services. In 2016 International Joint Conference on Neural Networks (IJCNN 2016), page ”to appear”.
Sarlan, A., Nadam, C., and Basri, S. (2014). Twitter sentiment analysis. pages 212–216.
Seerat, B. and Azam, F. (2012). Opinion mining: Issues and challenges (a survey). International Journal of Computer Applications, 49(9):42–51.
Sharma, A. and Dey, S. (2012a). An artificial neural network based approach for sentiment analysis of opinionated text. In Proceedings of the 2012 ACM Research in Applied Computation Symposium, pages 37–42.
Sharma, A. and Dey, S. (2012b). A document-level sentiment analysis approach using artificial neural network and sentiment lexicons. SIGAPP Appl. Comput. Rev., 12(4):67–75.
Silva, M. J., Carvalho, P., and Sarmento, L. (2012). Building a sentiment lexicon for social judgement mining. In Proceedings of the 10th International Conference on Computational Processing of the Portuguese Language, pages 218–228.
Tan, C., Lee, L., Tang, J., Jiang, L., Zhou, M., and Li, P. (2011). User-level sentiment analysis incorporating social networks. In Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 1397–1405.
Torruella, J. and Capsada, R. (2013). Lexical statistics and tipological structures: A measure of lexical richness. Procedia - Social and Behavioral Sciences, 95:447 – 454.
Vinodhini, G. and Chandrasekaran, R. (2012). Sentiment analysis and opinion mining: a survey. International Journal of Advanced Research in Computer Science and Software Engneering, 2(6).
Yannis, P. and Evanthia, K. (2012). An ontology-based question answering system exploiting search engines’ results. pages 426–429.
Publicado
05/07/2016
Como Citar
SANTOS, Roney L. de S.; MOURA, Raimundo S..
Extração de Métricas e Análise de Sentimentos em Comentários Web no Domínio de Hotéis. In: BRAZILIAN WORKSHOP ON SOCIAL NETWORK ANALYSIS AND MINING (BRASNAM), 2016. , 2016, Porto Alegre.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2016
.
p. 127-138.
ISSN 2595-6094.
DOI: https://doi.org/10.5753/brasnam.2016.6449.
