Lexicon-Based Methods for Sentiment Analysis

By Maite Taboada, Julian Brooke, Milan Tofiloski, Kimberly D. Voll, Manfred Stede. 2011

3,267 citations recorded by the source corpus.

We present a lexicon-based approach to extracting sentiment from text. The Semantic Orientation CALculator (SO-CAL) uses dictionaries of words annotated with their semantic orientation (polarity and strength), and incorporates intensification and negation. SO-CAL is applied to the polarity classification task, the process of assigning a positive or negative label to a text that captures the text's opinion towards its main subject matter. We show that SO-CAL's performance is consistent across domains and in completely unseen data. Additionally, we describe the process of dictionary creation, and our use of Mechanical Turk to check dictionaries for consistency and reliability.

Subfields: Artificial Intelligence

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Citations in the corpus

"Blissfully Happy" or "Ready toFight": Varying Interpretations of Emoji (2016, methodology): “Question three elicited a numeric sentiment judgment, mirroring the -5 to 5 sentiment scale used in Taboada et al. (2011).”
When does a compliment become sexist? Analysis and classification of ambivalent sexism using twitter data (2017, methodology): “We calculated the positive score and the negative score for each phrase in the tweet, us-ing SentiWordNet (Baccianella et al., 2010) and subjectivity lexicon (Taboada et al., 2011).”
Exploring Demographic Language Variations to Improve Multilingual Sentiment Analysis in Social Media (2013, background): “For polarity classification we distinguish between positive and negative instances, which is the approach typically reported in the literature for recognizing polarity (Velikovich et al., 2010; Yessenalina and Cardie, 2011; Taboada et al., 2011) A set-count feature is a count of the number of instan”
Adaptive Recursive Neural Network for Target-dependent Twitter Sentiment Classification (2014, background): “determines how to propagate the sentiments towards the target and handles the negation or intensification phenomena (Taboada et al., 2011) in sentiment analysis.”
Tensor Fusion Network for Multimodal Sentiment Analysis (2017, methodology): “Various approaches have been proposed to model sentiment from language, including methods that focus on opinionated words (Hu and Liu, 2004; Taboada et al., 2011; Poria et al., 2014b; Cambria et al., 2016), n-grams and language models (Yang and Cardie, 2012), sentiment compositionality and dependenc”
Attention-based LSTM Network for Cross-Lingual Sentiment Classification (2016, background): “There are two classes of mainstreaming sentiment classification algorithms: unsupervised methods which usually require a sentiment lexicon (Taboada et al., 2011) and supervised methods (Pang et al.”
Multimodal Sentiment Analysis: A Survey of Methods, Trends, and Challenges (2023, background): “[50] extended the semantic orientation calculator (SO-CAL) dictionary to predict the sentiment polarity.”
A review of verbal and non-verbal human-robot interactive communication (2014, background): “Furthermore, if one moves beyond prosodic affect, to semantic content, the wide literature on sentiment analysis and shallow identiﬁcation of affect applies directly; for example [121] [122] [123].”
Machine Learning-Based Sentiment Analysis for Twitter Accounts (2018, methodology): “Methods of sentiment analysis can be categorized predominantly [1] as machine-learning [2], Lexicon-based [3] and hybrid [4,5].”
Sentiment Analysis: Detecting Valence, Emotions, and Other Affectual States from Text (2016, methodology): “Earlier works on negation handling employed simple heuristics such as flipping the polarity of the words in a negator’s scope (Kennedy & Inkpen, 2005) or changing the degree of sentiment of the modified word by a fixed constant (Taboada et al., 2011).”
Turkers, Scholars, "Arafat" and "Peace": Cultural Communities and Algorithmic Gold Standards (2015, background): “Gold standard data support algorithms in a wide range of problem spaces ranging from sentiment analysis [40], to audio transcription [8], to machine translation [5].”
Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification (2014, methodology): “The lexicon-based approaches (Turney, 2002; Ding et al., 2008; Taboada et al., 2011; Thelwall et al., 2012) mostly use a dictionary of sentiment words with their associated sentiment polarity, and incorporate negation and intensification to compute the sentiment polarity for each sentence (or…”