A New ANEW: Evaluation of a Word List for Sentiment Analysis in Microblogs

By Finn Årup Nielsen. #MSM, 2011

1,391 citations recorded by the source corpus.

Sentiment analysis of microblogs such as Twitter has recently gained a fair amount of attention. One of the simplest sentiment analysis approaches compares the words of a posting against a labeled word list, where each word has been scored for valence, — a “sentiment lexicon” or “affective word lists”. There exist several affective word lists, e.g., ANEW (Affective Norms for English Words) developed before the advent of microblogging and sentiment analysis. I wanted to examine how well ANEW and other word lists performs for the detection of sentiment strength in microblog posts in comparison with a new word list specifically constructed for microblogs. I used manually labeled postings from Twitter scored for sentiment. Using a simple word matching I show that the new word list may perform better than ANEW, though not as good as the more elaborate approach found in SentiStrength.

Subfields: Artificial Intelligence

View Paper: https://www.semanticscholar.org/paper/d38763b4b0bbf1ed91b55107c658fcc96f8ef82d

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

Citations in the corpus

SenticNet 6: Ensemble Application of Symbolic and Subsymbolic AI for Sentiment Analysis (2020, methodology): “We used these six datasets to compare SenticNet 6 with 15 popular sentiment lexica, namely: ANEW [5], a list of 1,030 words created in 1999; WordNet-Affect [50], an extension of WordNet made of 4,787 words developed in 2004; Opinion Lexicon [22], a lexicon of 6,789 words built in the same year by me”
Temperature and temperament: Evidence from Twitter (2020, methodology): “The LIWC measure uses the Linguistic Inquiry and Word Count (LIWC) dictionary created by Pennebaker et al. (2015). Like AFINN and Hedonometer, LIWC uses a dictionarybased method to score text.”
"Let Me Tell You About Your Mental Health!": Contextualized Classification of Reddit Posts to DSM-5 for Web-based Intervention (2018, methodology): “( xii) Sentiment analysis is by comparing the words in a post to the word list with sentiment score in [31].”
Twitter Sentiment Analysis (2015, background): “Examples of the existing lexicons include: Opinion Lexicon (Hu and Liu, 2004), SentiWordNet (Esuli and Sebastiani, 2006), AFINN Lexicon (Nielsen, 2011), LoughranMcDonald Lexicon, NRC-Hashtag (Mohammad et al., 2013), General Inquirer Lexicon 3 (Stone and Hunt, 1963).”
Machine Learning Suites for Online Toxicity Detection (2018, methodology): “For English sentiment, we employed the AFINN-111 scale for positive or negative polarity (Nielsen, 2011).”
A survey on opinion mining and sentiment analysis: Tasks, approaches and applications (2015, background): “Word level 25 [26], [37], [45], [55], [57], [61], [67], [69], [73], [74], [86], [97], [99], [109], [110], [112], [114], [116], [118], [127], [163], [165], [166], [180], [184]”
Positive, Negative, or Neutral: Learning an Expanded Opinion Lexicon from Emoticon-Annotated Tweets (2015, background): “Afinn: This strength-oriented lexicon [Nielsen, 2011] has positive words scored from 1 to 5 and negative words scored from -1 to -5.”
Tuned Models of Peer Assessment in MOOCs (2013, methodology): “To measure the polarity of a comment, we use the sentiment analysis word list from [15] and implement a simple sentiment analyzer that returns a (normalized) polarity score (positive or negative) proportional to the sum of word valences over the comment.”
Examining Gender and Race Bias in Two Hundred Sentiment Analysis Systems (2018, background): “, 2014; Mohammad, 2018), AFINN (Nielsen, 2011), and Bing Liu Lexicon (Hu and Liu, 2004).”
SemEval-2018 Task 1: Affect in Tweets (2018, methodology): “Observe that several of the NRC emotion and sentiment lexicons as well as AFINN and Bing Liu Lexicon were widely used (Mohammad and Turney, 2013; Mohammad, 2018b; Kiritchenko et al., 2014; Nielsen, 2011; Hu and Liu, 2004).”
Like It or Not (2016, background): “…the one that is used in other text genres [Baeza-Yates and Rello 2011], researchers proposed the construction of new and [Bradley and Lang 1999], Nielsen [2011] proposed a new sentiment lexicon, known as the AFINN (after the author’s name Finn Årup Nielsen) lexicon, which was built for…”
A quantitative analysis of gender differences in movies using psycholinguistic normatives (2015, background): “…as the gender laden-ness are referred to as linguistic norms (or psycholinguistic norms when corresponding to psychological constructs) and are popular in cognitive psychology (Clark and Paivio, 2004) and some computational disciplines, such as sentiment analysis (Nielsen, 2011) and opinion mining.”