NILC-USP at SemEval-2017 Task 4: A Multi-view Ensemble for Twitter
            Sentiment Analysis

Júnior, Edilson Anselmo Corrêa; Marinho, Vanessa Queiroz; Santos, Leandro Borges dos

doi:10.18653/v1/s17-2100

Cited by 13 publications

(7 citation statements)

References 8 publications

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“…Isidoros Perikos and Ioannis Hatzilygeroudis [23] presented a sentiment analysis system for automatic recognition of emotions in text, using an ensemble model based on a Naï ve Bayes, a Maximum Entropy learner and a knowledge-based tool. Corrê a Jr. et al [24] …”

Section: Related Workmentioning

confidence: 99%

An Ensemble Model using a BabelNet Enriched Document Space for Twitter Sentiment Classification

Sevim¹,

Omurca²,

Ekinci³

2018

IJITCS

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Abstract-With the widespread usage of social media in our daily lives, user reviews emerged as an impactful factor for numerous fields including understanding consumer attitudes, determining political tendency, revealing strengths or weaknesses of many different organizations. Today, people are chatting with their friends, carrying out social relations, shopping and following many current events through the social media. However social media limits the size of user messages. The users generally express their opinions by using emoticons, abbreviations, slangs, and symbols instead of words. This situation makes the sentiment classification of social media texts more complex. In this paper a sentiment classification model for Twitter messages is proposed to overcome this difficulty. In the proposed model first the short messages are expanded with BabelNet which is a concept network. Then the expanded and the original form of the messages are included in an ensemble learning model. Consequently we compared our ensemble model with traditional classification algorithms and observed that the F-measure value is increased.

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Section: Related Workmentioning

confidence: 99%

An Ensemble Model using a BabelNet Enriched Document Space for Twitter Sentiment Classification

Sevim¹,

Omurca²,

Ekinci³

2018

IJITCS

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%

“…All the above techniques do not consider context or semantic similarity of words during classification. The context of words can be incorporated in a machine learning algorithm using word embedding [18]. This helps machine learning algorithms to learn densely distributed representation for each word.…”

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%

“…Contents features are widely used for sentiment classification [13], [18], [19]. There are many ways to model the content features like bag of words model (BOW), n-grams, etc.…”

Section: Context-aware Sliding Window (Csw)mentioning

confidence: 99%

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Context-Aware Sliding Window for Sentiment Classification

Masood

Abbasi

2020

IEEE Access

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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.

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Unsupervised approaches for measuring textual similarity between legal court case reports

Mandal

Ghosh

et al. 2021

Artif Intell Law

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NILC-USP at SemEval-2017 Task 4: A Multi-view Ensemble for Twitter Sentiment Analysis

Cited by 13 publications

References 8 publications

An Ensemble Model using a BabelNet Enriched Document Space for Twitter Sentiment Classification

An Ensemble Model using a BabelNet Enriched Document Space for Twitter Sentiment Classification

Context-Aware Sliding Window for Sentiment Classification

Unsupervised approaches for measuring textual similarity between legal court case reports

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