Sentiment Analysis on Twitter Data for Portuguese Language

Souza, Marlo; Vieira, Renata

doi:10.1007/978-3-642-28885-2_28

Cited by 47 publications

(32 citation statements)

References 19 publications

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“…Most literature on sentiment analysis for Portuguese language addresses polarity classification at sentence and aspect (feature) level. In applications at the sentence-level, in which sentences are classified as positive, negative, or neutral, the accuracy ranges from 55 % to 71,79 % [6][7][8]17]. In these applications, the best results were obtained from the Sequential Minimal Optimization (SMO) [6,7] and Naive Bayes [3] algorithms.…”

Section: Related Workmentioning

confidence: 99%

Comparing Approaches to Subjectivity Classification: A Study on Portuguese Tweets

Moraes

Santos

Redecker

et al. 2016

Lecture Notes in Computer Science

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Abstract. In this paper, we compare lexicon-based and machine learning-based approaches to define the subjectivity of tweets in Portuguese. We tested SentiLex and WordAffectBR lexicons, and Sequential Machine Optimization and Naive Bayes algorithms for this task. In our study, we used the Computer-BR corpus that contains messages about the technology area. We obtained better results using the Comprehensive Measurement Feature Selection method and the Sequential Machine Optimization algorithm as the classifier. We achieved considerable accuracy when we included the polarities of words in the vector space model of tweets.

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Section: Related Workmentioning

confidence: 99%

Comparing Approaches to Subjectivity Classification: A Study on Portuguese Tweets

Moraes

Santos

Redecker

et al. 2016

Lecture Notes in Computer Science

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“…In [12] the authors propose a lexicon-based classifier for tweets in BP. Both lexicons OpinionLexicon and SentiLex are used.…”

Section: Related Workmentioning

confidence: 99%

Lexicon-Based Sentiment Analysis for Reviews of Products in Brazilian Portuguese

Avanço¹,

Nunes²

2014

2014 Brazilian Conference on Intelligent Systems

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Abstract-This paper presents some results on lexicon-based classification of sentiment polarity in web reviews of products written in Brazilian Portuguese. They represent a first step towards a robust opinion miner from reviews of technology products. The evaluation shows the performance of 3 different sentiment lexicons combined with simple strategies. It is also discussed the risk of considering the rating provided by the writers for the purpose of evaluating the algorithms. The results show that the better combination is the version of the algorithm that deals also with negation and intensification and uses the sentiment lexicon Sentilex. The average F-measure achieved 0.73.

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“…Therefore, to capture and understand users' experiences in social media or to facilitate the design of human-centric services by social media [6], users' opinions about specific entities in text messages should be understood and classified into discrete categories, such as good/bad, pleasant/unpleasant, or positive/negative/neutral, in advance. This research area is called entity-based sentiment analysis (or entity-centric sentiment analysis) in natural language processing (NLP) [4,8,16]. In entity-based sentiment analysis, users' opinions are usually targeted at named entities, such as Michael Jackson, Microsoft, and Avengers.…”

mentioning

confidence: 99%

Semi-automatic construction of a named entity dictionary for entity-based sentiment analysis in social media

Song

Jeong

Kim

2016

Multimed Tools Appl

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To understand the user experience in social media or to facilitate the design of human-centric services by social media, users' opinions about specific entities in text messages should be captured. A fine-grained named entity recognizer (NER) is an essential module for identifying opinion targets in text messages, and a named-entity (NE) dictionary is a major resource that affects the performance of an NER. However, it is not easy to construct an NE dictionary manually, because human annotation is time-consuming and labor-intensive. To reduce construction time and labor, we propose a semi-automatic system to construct an NE dictionary from the free online resource, Wikipedia. The proposed system constructs a pseudodocument for each Wikipedia NE by using an active-learning technique. It then classifies Wikipedia entries into NE classes based on similarities between the entries and pseudodocuments located in a vector space. In experiments, the proposed system classified 92.3 % of Wikipedia entries into 29 NE classes. It showed a high performance, with a macroaveraging F1-measure of 0.872 and micro-averaging F1-measure of 0.935.

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Sentiment Analysis on Twitter Data for Portuguese Language

Cited by 47 publications

References 19 publications

Comparing Approaches to Subjectivity Classification: A Study on Portuguese Tweets

Comparing Approaches to Subjectivity Classification: A Study on Portuguese Tweets

Lexicon-Based Sentiment Analysis for Reviews of Products in Brazilian Portuguese

Semi-automatic construction of a named entity dictionary for entity-based sentiment analysis in social media

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