Robust Sentiment Detection on Twitter from Biased and Noisy Data

By Luciano Barbosa, Junlan Feng. International Conference on Computational Linguistics, 2010

1,014 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/aa22092e0d46566855eb1b8f72c5786dc2000d77

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Citations in the corpus

Emoticon Smoothed Language Models for Twitter Sentiment Analysis (2012, methodology): “(Barbosa and Feng 2010) uses the noisy data collected from some Twitter sentiment detection web sites, such as the Twitter Sentiment 3 .”
Alleviating Data Sparsity for Twitter Sentiment Analysis (2012, background): “Barbosa and Feng [2] collected their training data from three different Twitter sentiment detection websites which mainly use some pre-built sentiment lexicons to label each tweet as positive or negative.”
Deep Convolutional Neural Networks for Sentiment Analysis of Short Texts (2014, background): “The advent of online social networks has produced a crescent interest on the task of sentiment analysis for short text messages (Go et al., 2009; Barbosa and Feng, 2010; Nakov et al., 2013).”
Exploring Demographic Language Variations to Improve Multilingual Sentiment Analysis in Social Media (2013, background): “…has also been a considerable amount of work in subjectivity and sentiment analysis over the past decade, including, more recently, in microblogs (Barbosa and Feng, 2010; Bermingham and Smeaton, 2010; Pak and Paroubek, 2010; Bifet and Frank, 2010; Davidov et al., 2010; Li et al., 2010;…”
Large Scale Sentiment Analysis on Twitter with Spark (2016, background): “Their usage is twofold: they provide categorization of a message and/or highlight of a topic and they enhance the searching of tweets that refer to a common subject.”
Global Belief Recursive Neural Networks (2014, background): “Sentiment analysis has been the subject of research for some time [4, 2, 3, 6, 1, 23].”
Twitter Sentiment Analysis (2015, unknown): “Barbosa et al. reports better results for SVMs (Barbosa and Feng, 2010) while Pak et al. obtained better results for Naive Bayes (Pak and Paroubek, 2010).”
Sentiment Analysis on Twitter (2012, methodology): “…path to follow, this recent trend for research for sentiment mining in twitter can be utilized and extended for a gamut of practical applications that range from applications in business (marketing intelligence; product and service bench marking and improvement), applications as subcomponent…”
Contextual semantics for sentiment analysis of Twitter (2016, background): “…various combinations of features, such as word n-grams (Go et al., 2009; Pak and Paroubek, 2010; Bifet and Frank, 2010), Part-Of-Speech (POS) tags (Barbosa and Feng, 2010; Agarwal et al., 2011) with or without words’ prior sentiment, words’ semantic concepts (Saif et al., 2012b),…”
Part-of-Speech Tagging for Twitter: Annotation, Features, and Experiments (2010, background): “com) is one particularly fruitful source of user-created content, and a flurry of recent research has aimed to understand and exploit these data (Ritter et al., 2010; Sharifi et al., 2010; Barbosa and Feng, 2010; Asur and Huberman, 2010; O’Connor et al., 2010a; Thelwall et al., 2011).”
Sentiment analysis in twitter using machine learning techniques (2013, background): “Since social network generates huge amount of data every second, sometimes larger than the size of available lexical database, sentiment analysis became tedious and erroneous.”
A survey of sentiment analysis in social media (2018, background): “Barbosa et al. [12] proposed an approach automatically detecting sentiments on Tweets, which explores some traits of how tweets are written, and meta-information of the words composing these messages.”
SemEval-2015 Task 10: Sentiment Analysis in Twitter (2015, background): “searchers (Barbosa and Feng, 2010; Bifet et al., 2011; Davidov et al., 2010; Jansen et al., 2009; Kouloumpis et al., 2011; O’Connor et al., 2010; Pak and Paroubek, 2010).”
Sentiment Analysis of Twitter Data : A Survey of Techniques (2016, methodology): “or Machines (SVM). Their feature space consisted of unigrams, bigrams and POS. They concluded that SVM outperformed other models and that unigram were more effective as features. Barbosa et al.(2010) [4] designed a two phase automatic sentiment analysis method for classifying tweets. They classified”
User-level sentiment analysis incorporating social networks (2011, background): “RELATED WORK Recently, there has been some work on sentiment analysis on Twitter, focusing on the tweet level [10, 13, 3, 4, 8].”
Combining lexicon-based and learning-based methods for twitter sentiment analysis (2011, unknown): “(Barbosa and Feng, 2010) proposed a twostep classification method.”