2017
DOI: 10.1016/j.patrec.2017.05.024
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A simple approach to multilingual polarity classification in Twitter

Abstract: Recently, sentiment analysis has received a lot of attention due to the interest in mining opinions of social media users. Sentiment analysis consists in determining the polarity of a given text, i.e., its degree of positiveness or negativeness. Traditionally, Sentiment Analysis algorithms have been tailored to a specific language given the complexity of having a number of lexical variations and errors introduced by the people generating content. In this contribution, our aim is to provide a simple to implemen… Show more

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Cited by 33 publications
(16 citation statements)
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“…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%
“…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%
“…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
“…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%