Sobre normalização e classificação de polaridade de textos opinativos na web

Avanço, Lucas

doi:10.11606/d.55.2016.tde-24032016-171420

Cited by 5 publications

(17 citation statements)

References 49 publications

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“…Using this combination of convolutional and recurrent layers, we explored the principle that nearby words have a greater influence in the classification, while distant words may also have some impact. d) Hybrid: This method is a combination of two classifiers previously used for sentiment classification in crossdomain corpora [9] and follows the same setting introduced by Avanço [33]. The method consists of a SVM classifier combined with a lexical-based approach.…”

Section: B Machine Learning Methodsmentioning

confidence: 99%

PELESent: Cross-Domain Polarity Classification Using Distant Supervision

Corrêa

Marinho

Santos

et al. 2017

2017 Brazilian Conference on Intelligent Systems (BRACIS)

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The enormous amount of texts published daily by Internet users has fostered the development of methods to analyze this content in several natural language processing areas, such as sentiment analysis. The main goal of this task is to classify the polarity of a message. Even though many approaches have been proposed for sentiment analysis, some of the most successful ones rely on the availability of large annotated corpus, which is an expensive and time-consuming process. In recent years, distant supervision has been used to obtain larger datasets. So, inspired by these techniques, in this paper we extend such approaches to incorporate popular graphic symbols used in electronic messages, the emojis, in order to create a large sentiment corpus for Portuguese. Trained on almost one million tweets, several models were tested in both same domain and cross-domain corpora. Our methods obtained very competitive results in five annotated corpora from mixed domains (Twitter and product reviews), which proves the domain-independent property of such approach. In addition, our results suggest that the combination of emoticons and emojis is able to properly capture the sentiment of a message.

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Section: B Machine Learning Methodsmentioning

confidence: 99%

PELESent: Cross-Domain Polarity Classification Using Distant Supervision

Corrêa

Marinho

Santos

et al. 2017

2017 Brazilian Conference on Intelligent Systems (BRACIS)

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“…Para melhor ilustrar esse fenômeno, a Figura 1 apresenta uma review de um produto escrita por um usuário comum. Fonte: Avanço (2015).…”

Section: Lista De Ilustraçõesunclassified

“…Muito do CGU contém opiniões, sendo que a principal fonte de conteúdo opiniativo são as avaliações -que podem ser sobre produtos, filmes, músicas ou qualquer outra entidade sobre qual usuários possam opinar. Sendo assim, a análise de sentimentos pode fazer uso de CGU em diversas aplicações práticas, como: previsão de desempenho de vendas, análise de postagens no Twitter para entender o comportamento do mercado, distribuição estratégica de propagandas em páginas Web, previsão de resultados das eleições e análise de posicionamento partidário (AVANÇO, 2015). Embora as aplicações sejam promissoras, lidar com CGU oferece desafios para PLN.…”

Section: Conceitos Definições E Aplicaçõesunclassified

“…Este trabalho, que é decorrente de Avanço (2015), propõe um sistema de normalização para textos de CGU escritos em português brasileiro. O sistema é restrito ao domínio de reviews de produtos, sendo que sua construção é baseada no córpus descrito em Hartmann et al (2014).…”

Section: Trabalho De Duran Avanço E Nunes (2015)unclassified

“…O módulo de tokenização foi projetado especificamente para textos da web. Segundo Avanço (2015), o uso de um tokenizador tradicional acarreta perda de termos importantes, como unidades de medida (30mb, 7gb) e emoticons ( :) , :-/). Considerando essas características, o tokenizador do UGCNormal foi desenvolvido de modo a identificar emoticons, unidades de medida e URLs.…”

Section: Trabalho De Duran Avanço E Nunes (2015)unclassified

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Normalização textual de conteúdo gerado por usuário

Bertaglia¹

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User Generated Content (UGC) is the name given to content created spontaneously by ordinary individuals, without connections to the media. This type of content carries valuable information and can be exploited by several areas of knowledge. Much of the UGC is provided in the form of texts -product reviews, comments on forums about movies, and discussions on social networks are examples. However, the language used in UGC texts differs, in many ways, from the cultured norm of the language, making it difficult for NLP techniques to handle them. UGC language is strongly linked to the language used in daily life, containing a large amount of noise. Spelling mistakes, abbreviations, slang, absence or misuse of punctuation and capitalization are some noises that make it difficult to process these texts. Several works report considerable loss of performance when testing NLP state-of-the-art tools in UGC texts. Textual Normalization is the process of turning noisy words into words considered correct and can be used to improve the quality of UGC texts. This work reports the development of methods and systems that aim to (a) identify noisy words in UGC, (b) find candidate words for substitution, and (c) rank candidates for normalization. For the identification of noisy words, lexical-based methods and machine learning ones using deep neural networks were proposed. The automatic identification presented results comparable to the use of lexicons, proving that this process can be done with low dependence of resources. For the generation and ranking of candidates, techniques based on lexical similarity and word embeddings were investigated. It was concluded that the use of embeddings is highly suitable for normalization, having achieved the best results. All proposed methods were evaluated based on a UGC corpus annotated throughout the project, containing texts from different sources: discussion forums, product reviews and tweets. A system, Enelvo, combining all methods was implemented and compared to another existing normalizing system, UGCNormal. The results obtained by the Enelvo system were considerably higher, with a correction rate between 67 % and 97 % for different types of noise, with less dependence on resources and greater flexibility in normalization.

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Expansão de recursos para análise de sentimentos usando aprendizado semi-supervisionado

Brum¹

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Dedico esse trabalho a Marcia Cristia Cera. Uma vez ela dançou uma música ridícula na sala pra ensinar um conteúdo. Sempre soube que existiam bons professores, mas ali eu percebi que pra ser o melhor tem que amar muito o que faz. Ela amou. E foi. RESUMO BRUM, H. B. Expansão de recursos para análise de sentimentos usando aprendizado semissupervisionado. 2018. 173 p. Dissertação (Mestrado em Ciências-Ciências de Computação e

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Sobre normalização e classificação de polaridade de textos opinativos na web

Cited by 5 publications

References 49 publications

PELESent: Cross-Domain Polarity Classification Using Distant Supervision

PELESent: Cross-Domain Polarity Classification Using Distant Supervision

Normalização textual de conteúdo gerado por usuário

Expansão de recursos para análise de sentimentos usando aprendizado semi-supervisionado

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