Sentiment analysis system adaptation for multilingual processing: The case of tweets

Balahur, Alexandra; Perea‐Ortega, José M.

doi:10.1016/j.ipm.2014.10.004

Cited by 44 publications

(20 citation statements)

References 6 publications

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“…Another problem faced by these approaches is that some languages are poorer in language resources, which are costly to create. On the other hand, studies like Banea, Mihalcea and Wiebe (2010) and Balahur and Perea-Ortega (2015) indicate that the combination of multilingual features tends to produce better results than the monolingual ones.…”

Section: Sentiment Analysismentioning

confidence: 98%

Clustering Multilingual Aspect Phrases for Sentiment Analysis

Pessutto

Vargas

Moreira

2018

2018 IEEE/WIC/ACM International Conference on Web Intelligence (WI)

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Agradeço aos meus pais Sérgio e Tania, ao meu irmão Leonardo, a minha família e todos os meus amigos. Obrigado pelo incentivo, paciência e por estarem do meu lado durante toda a minha jornada. Agradeço a minha orientadora, Prof. Viviane Moreira, que durante este trabalho contribuiu com seu vasto conhecimento e experiência. Mais do que isto, seu incentivo foi indispensável em diversos momentos durante a execução deste trabalho. Dedico este trabalho a todos os professores que participaram da minha formação, desde os que me ensinaram a ler e escrever, aos que me ensinaram a programar, e principalmente aos que me fizeram gostar de ciência. Graças a profissionais tão brilhantes este trabalho pode se tornar realidade. Agradeço aos meus colegas de curso, por deixarem mais fácil e mais leve a minha caminhada e pelas trocas de conhecimento e experiência que tivemos. Agradeço também à toda a comunidade do INF-UFRGS que se empenham pra garantir uma formação de excelência aos seus estudantes. E finalmente agradeço a Deus por ter proporcionado estes agradecimentos a todos que tornaram minha vida melhor, além de ter me dado uma família maravilhosa e amigos sinceros.

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Section: Sentiment Analysismentioning

confidence: 98%

Clustering Multilingual Aspect Phrases for Sentiment Analysis

Pessutto

Vargas

Moreira

2018

2018 IEEE/WIC/ACM International Conference on Web Intelligence (WI)

View full text Add to dashboard Cite

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“…Here, the feature is associated with the bigrams in the document. Further, in n-gram representation, "N" number of words in the document were considered [85].…”

Section: Sentiment Analysis In Gram Representationmentioning

confidence: 99%

Emotion AI-Driven Sentiment Analysis: A Survey, Future Research Directions, and Open Issues

et al. 2019

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The essential use of natural language processing is to analyze the sentiment of the author via the context. This sentiment analysis (SA) is said to determine the exactness of the underlying emotion in the context. It has been used in several subject areas such as stock market prediction, social media data on product reviews, psychology, judiciary, forecasting, disease prediction, agriculture, etc. Many researchers have worked on these areas and have produced significant results. These outcomes are beneficial in their respective fields, as they help to understand the overall summary in a short time. Furthermore, SA helps in understanding actual feedback shared across different platforms such as Amazon, TripAdvisor, etc. The main objective of this thorough survey was to analyze some of the essential studies done so far and to provide an overview of SA models in the area of emotion AI-driven SA. In addition, this paper offers a review of ontology-based SA and lexicon-based SA along with machine learning models that are used to analyze the sentiment of the given context. Furthermore, this work also discusses different neural network-based approaches for analyzing sentiment. Finally, these different approaches were also analyzed with sample data collected from Twitter. Among the four approaches considered in each domain, the aspect-based ontology method produced 83% accuracy among the ontology-based SAs, the term frequency approach produced 85% accuracy in the lexicon-based analysis, and the support vector machine-based approach achieved 90% accuracy among the other machine learning-based approaches.

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“…Another approach that is built for two languages is presented in [21]. SA application is shown for English and Spanish languages using multilingual hybrid features and machine translated data.…”

Section: Related Workmentioning

confidence: 99%

Feature-based Sentiment Analysis for Slang Arabic Text

Abdallah¹,

Abo-Suaileek²

2019

IJACSA

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The increased number of Arab users on microblogging services who use Arabic language to write and read has triggered several researchers to study the posted data and discover the user's opinion and feelings to support decision making. In this paper, a sentiment analysis framework is presented for slang Arabic text. A new dataset with Jordanian dialect is presented. Numerous specific Arabic features are shown with their impact on slang Arabic Tweets. The new set of features consists of lexicon, writing style, grammatical and emotional features. Several experiments are conducted to test the performance of the proposed scheme. The new proposed scheme produces better results in comparison with others. The experiments show that the system performs well without translating the tweets to English or standard Arabic.

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Sentiment analysis system adaptation for multilingual processing: The case of tweets

Cited by 44 publications

References 6 publications

Clustering Multilingual Aspect Phrases for Sentiment Analysis

Clustering Multilingual Aspect Phrases for Sentiment Analysis

Emotion AI-Driven Sentiment Analysis: A Survey, Future Research Directions, and Open Issues

Feature-based Sentiment Analysis for Slang Arabic Text

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