A simple approach to multilingual polarity classification in Twitter

Téllez, Eric Sadit; Miranda-Jiménez, Sabino; Graff, Mario; Moctezuma, Daniela; Surez, Ranyart R.; Siordia, Oscar S.

doi:10.1016/j.patrec.2017.05.024

Cited by 32 publications

(16 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…In sentiment analysis task we compared the datasets reported in [32,33]. Moreover, we reported the results obtained with the B4MSA approach [34]. B4MSA is a method for multilingual polarity classification considered as a baseline to build more complex approaches 15 .…”

Section: Resultsmentioning

confidence: 99%

An automated text categorization framework based on hyperparameter optimization

Téllez

Moctezuma

Miranda-Jiménez

et al. 2018

Knowledge-Based Systems

Self Cite

View full text Add to dashboard Cite

A great variety of text tasks such as topic or spam identification, user profiling, and sentiment analysis can be posed as a supervised learning problem and tackle using a text classifier. A text classifier consists of several subprocesses, some of them are general enough to be applied to any supervised learning problem, whereas others are specifically designed to tackle a particular task, using complex and computational expensive processes such as lemmatization, syntactic analysis, etc. Contrary to traditional approaches, we propose a minimalistic and wide system able to tackle text classification tasks independent of domain and language, namely µTC. It is composed by some easy to implement text transformations, text representations, and a supervised learning algorithm. These pieces produce a competitive classifier even in the domain of informally written text. We provide a detailed description of µTC along with an extensive experimental comparison with relevant state-of-the-art methods. µTC was compared on 30 different datasets. Regarding accuracy, µTC obtained the best performance in 20 datasets while achieves competitive results in the remaining 10. The compared datasets include several problems like topic and polarity classification, spam detection, user profiling and authorship attribution. Furthermore, it is important to state that our approach allows the usage of the technology even without knowledge of machine learning and natural language processing.

show abstract

Section: Resultsmentioning

confidence: 99%

An automated text categorization framework based on hyperparameter optimization

Téllez

Moctezuma

Miranda-Jiménez

et al. 2018

Knowledge-Based Systems

Self Cite

View full text Add to dashboard Cite

show abstract

“…Similarly, some researchers proposed to apply textual features like n-grams, bag of words (BOW), and term frequency-inverse document frequency (TF-IDF) in machine learning algorithms to classify sentiments [13], [18], [19]. On the other hand, some researchers devised a hybrid technique, which combines lexicon and machine learning together to recommend sentiments [14], [16], [20].…”

Section: B Machine Learning Based Sentiment Classificationmentioning

confidence: 99%

“…Over the past, machine learning and lexicon-based techniques have been used for sentiment classification. Some researchers modeled features to enhance the functionality of the learning algorithms [13], [18], [19]. However, feature modeling requires careful shortlisting of the discriminating features; Deep learning automatically learns discriminating features from the data.…”

Section: Deep Learning Sentiment Classificationmentioning

confidence: 99%

See 1 more Smart Citation

Context-Aware Sliding Window for Sentiment Classification

Masood

Abbasi

2020

IEEE Access

View full text Add to dashboard Cite

Sentiment classification is an active area of research with applications in many domains. Many researchers in the past have proposed techniques to identify sentiments with reasonable accuracy. However, the focus is more on the syntactic and semantic features of the documents. These features are effective but they ignore the user's past sentiments. In this research, we hypothesize that the past sentiments help the classifier to effectively link the user's history along with the contents of the current tweet. Thus, allowing learning algorithms to correlate past activities in determining the current sentiments. For this sake, we propose three sliding window features to accumulate past sentiments from the time series data. In this paper, we propose seven variations of Context-aware Sliding Window (CSW) features on different machine learning and deep learning algorithms. Furthermore, we propose a temporal dataset of user tweets, which is manually labeled by nine human annotators. The proposed dataset consists of 36 users having 4,557 tweets. Results indicate significant improvements over six state-of-the-art baseline methods. INDEX TERMS Sentiment classification, user history, temporal sliding window, deep learning, machine-learning, temporal dataset.

show abstract

“…They also use the model to identify the target of the opinions, the product or the video itself. Tellez et al (2017) proposed an extensive list of features that can be used to polarity assignment. These features were grouped in two sets -the cross-lingual and the language dependent.…”

Section: Multilingual Sentiment Analysismentioning

confidence: 99%

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

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.

show abstract

A simple approach to multilingual polarity classification in Twitter

Cited by 32 publications

References 13 publications

An automated text categorization framework based on hyperparameter optimization

An automated text categorization framework based on hyperparameter optimization

Context-Aware Sliding Window for Sentiment Classification

Clustering Multilingual Aspect Phrases for Sentiment Analysis

Contact Info

Product

Resources

About