Sentiment Analysis of Twitter Data

By Apoorv Agarwal, Boyi Xie, Ilia Vovsha, Owen Rambow, Rebecca J. Passonneau. 2011

2,259 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/a787499299fa75a7d8597aa4793255e9587a3c7f

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Citations in the corpus

Sentiment Analysis of Short Informal Texts (2014, background): “…versus informality, length of utterances, etc. Sentiment analysis systems developed speciﬁcally for tweets include those by Pak and Paroubek (2010), Agarwal, Xie, Vovsha, Rambow, and Passonneau (2011), Thelwall, Buckley, and Paltoglou (2011), Brody and Diakopoulos (2011), Aisopos, Papadakis,…”
A comprehensive survey on model compression and acceleration (2020, background): “…of RNNs has come like speech recognition (Graves and Schmidhuber 2005; Graves et al. 2013), language translation (Sutskever et al. 2014), text generation (Sutskever et al. 2011), sentiment analysis (Agarwal et al. 2011), natural language processing (NLP) (Collobert et al. 2011) to name a few.”
Sentiment Analysis: Detecting Valence, Emotions, and Other Affectual States from Text (2016, unknown): “, 2009; Genereux & Evans, 2006; Mihalcea & Liu, 2006), and tweets (Pak & Paroubek, 2010; Agarwal et al., 2011; Thelwall et al., 2011; Brody & Diakopoulos, 2011; Aisopos et al., 2012; Bakliwal et al., 2012; Mohammad, 2012a).”
Target-Dependent Twitter Sentiment Classification with Rich Automatic Features (2015, methodology): “…model by manually adding more complex features, such as proposing salience measures to discriminate common N-grams [Pak and Paroubek, 2010], extracting pattern-based features [Davidov et al., 2010], building a tree kernel [Agarwal et al., 2011] or using lexicon features [Kouloumpis et al., 2011].”
Semantic Frames to Predict Stock Price Movement (2013, background): “Other resources for sentiment detection include the Dictionary of Affect in Language (DAL) to score the prior polarity of words, as in Agarwal et al. (2011) on social media data.”
Opinion Mining and Information Fusion: A survey (2016, background): “The first is through a website's Application Programming Interface (API) being Twitter's 2 one of the most popular [22] [23] [24] [25].”
Challenges in Sentiment Analysis (2017, methodology): “Other sentiment analysis systems developed speciﬁcally for tweets include those by Pak and Paroubek (2010), Agarwal, Xie, Vovsha, Rambow, and Passonneau (2011), Thelwall, Buckley, and Paltoglou (2011), Brody and Diakopoulos (2011), Aisopos, Papadakis, Tserpes, and Varvarigou (2012), Bakliwal,…”
VADER: A Parsimonious Rule-Based Model for Sentiment Analysis of Social Media Text (2014, result): “Other syntactical considerations identified via qualitative analysis (negation, degree modifiers, and contrastive conjunctions) also help make VADER successful, and is consistent with prior work (Agarwal et al., 2011; Ding, Liu, & Yu, 2008; Lu, Castellanos, Dayal, & Zhai, 2011; Socher et al., 2013).”
A survey on opinion mining and sentiment analysis: Tasks, approaches and applications (2015, unknown): “S# Tasks and applications #Articles References 1 Subjectivity Classification 6 [44], [75], [110], [163], [167], [174] 2 Polarity determination 43 [12], [26], [29], [32], [33], [35], [40], [45], [48], [50], [54], [57], [66], [85], [95], [96], [108], [109], [112], [114], [123], [126], [154], [156], [1”
Sentiment analysis of Twitter data for predicting stock market movements (2016, background): “Tweets are classiﬁed as positive, negative and neutral based on the sentiment present [18].”
That’s So Annoying!!!: A Lexical and Frame-Semantic Embedding Based Data Augmentation Approach to Automatic Categorization of Annoying Behaviors using #petpeeve Tweets (2015, background): “, 2010b), sentiment analysis (Agarwal et al., 2011), event extraction (Ritter et al.”
Predicting Fluctuations in Cryptocurrency Transactions Based on User Comments and Replies (2016, background): “Many past studies have dealt with classifying user sentiment or comment data [15, 30 – 35].”
A deep learning approach for detecting traffic accidents from social media data (2018, methodology): “If we employ RNN for a supervised learning task in language modeling, the process can be described as using a sequence of word features to predict the manual labels such as topics, sentiment (Agarwal et al., 2011), etc.”
When does a compliment become sexist? Analysis and classification of ambivalent sexism using twitter data (2017, methodology): “To remove the need for feature engineering, Agarwal et al. (2011) use POS-speciﬁc prior polarity features and tree kernel for sentiment analysis.”
Twitter brand sentiment analysis: A hybrid system using n-gram analysis and dynamic artificial neural network (2013, methodology): “…representations in the sentiment analysis various machine learning techniques have been evaluated by researchers, although support vector machines (Agarwal et al., 2011; Barbosa & Feng, 2010; Bermingham & Smeaton, 2010; Go et al., 2009; Jiang et al., 2011; Zhang et al., 2011) and naïve Bayes…”
Sentiment Analysis of Twitter Data : A Survey of Techniques (2016, methodology): “Agarwal et al. (2011)[6] developed a 3-way model for classifying sentiment into positive, negative and neutral classes.”
Semantic Sentiment Analysis of Twitter (2012, methodology): “While previous work on feature engineering for sentiment classification on tweets [1, 6] simply incorporate features through augmentation, our experimental results show that it is more effective to incorporate semantic features through interpolation.”