AFINN

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AFINN
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Category: AFINN
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Word list
Text sentiment analysis

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AFINN is an affective lexicon by Finn Årup Nielsen. Previous and present versions of the word list are available in a zip file:

http://www2.imm.dtu.dk/pubdb/views/edoc_download.php/6010/zip/imm6010.zip

The word list and its evaluation are described in:

A new ANEW: evaluation of a word list for sentiment analysis in microblogs

Although the title of the associated paper suggests that it is based on the ANEW labeled corpus it is not (the title is simply a wordpun). It was developed independently of the wordlist, and it is not a revision of it. Compared to ANEW, the AFINN word list has more word and includes obscene words. ANEW on the other hand has (besides valence) arousal and dominance for each word and each word has been labeled by several persons and the mean and standard deviation are given. The AFINN was only labeled by Finn Årup Nielsen.

The word list has been used for sentiment analysis and is developed in the Responsible Business in the Blogosphere project.

Contents

[edit] Evaluation

  1. Correlation with Alan Mislove's 1000 AMT-labeled tweet post, see A new ANEW: evaluation of a word list for sentiment analysis in microblogs
  2. Correlation with 50 positive and negative manually labeled tweets: 76%-78%, see Analyzing emotion on Twitter for using modeling
  1. Around 0.55 in three-class accuracy on RepLab Twitter data set. However, this is a combined list consisting of AFINN, SentiWordNet and Liu wordlist used as features in a decision tree machine learning classifier.[1]

[edit] See also

[edit] Papers

  1. A new ANEW: evaluation of a word list for sentiment analysis in microblogs. Evaluation of the word list with 2477 words.

[edit] Applications of the word list

  1. 2012 presidential elections on Twitter - an analysis of how the US and French election were reflected in tweets
  2. A framework for knowledge derivation incorporating trust and quality of data (2013)
  3. A fine-grained sentiment analysis approach for detecting crisis related microposts (2013)
  4. A longitudinal study of follow predictors on Twitter (2013)
  5. A novel transit rider satisfaction metric: rider sentiments measured from online social media data
  6. Aesthetic considerations for automated platformer design (2012)
  7. Audience targeting by B-to-B advertisement classification: a neural network approach
  8. BOUNCE: sentiment classification in Twitter using rich feature sets (2013)
  9. Capturing place semantics from users' interaction on the geosocial web (2014)
  10. Catching fish in the stream: real time analysis of audience behavior in social media (2013)
  11. Character-to-character sentiment analysis in Shakespeare's plays (2013)
  12. Combining strengths, emotions and polarities for boosting Twitter sentiment analysis
  13. Contradiction detection between opinions (2013)
  14. Cooperative, dynamic Twitter parsing and visualization for dark network analysis (2013)
  15. Crowd sentiment detection during disasters and crises
  16. Cues to deception in social media communications
  17. Discovering content-based behavioral roles in social networks
  18. Enhancing lexicon-based review classification by merging and revising sentiment dictionaries
  19. Exploratory search on Twitter utilizing user feedback and multi-perspective microblog analysis
  20. Extracting sentiment networks from Shakespeare's plays
  21. FBM-Yahoo! at RepLab 2012 (2012)
  22. Forex-Foreteller: currency trend modeling using news articles
  23. Full-FACE poetry generation
  24. Geo-spatial event detection in the Twitter stream
  25. Good friends, bad news - affect and virality in Twitter (2011)
  26. Identifying consumers' arguments in text (2012)
  27. In search of reputation assessment: experiences with polarity classification in RepLab 2013 (2013)
  28. KLUE: simple and robust methods for polarity classification
  29. Lexvo.org: language-related information for the Linguistic Linked Data Cloud
  30. Mining Facebook data for predictive personality modeling
  31. Networks and language in the 2010 election
  32. NTNU: domain semi-independent short message sentiment classification
  33. Privacy nudges for social media: an exploratory Facebook study (2013)
  34. Probing of geospatial stream data to report disorientation (2013)
  35. Real-time monitoring of sentiment in business related Wikipedia (2013)
  36. Rule-based visual mappings -- with a case study on poetry visualization (2013)
  37. Representing and resolving negation for sentiment analysis (2012)
  38. Sarcasm as contrast between a positive sentiment and negative situation (2013)
  39. Semi-automated argumentative analysis of online product reviews (2012)
  40. Some remarks on the internal consistency of online consumer reviews (2013)
  41. Suicidal tendencies: the automatic classification of suicidal and non-suicidal lyricists using NLP (2013)
  42. Summarization of yes/no questions using a feature function model (2011)
  43. The lexicon-based sentiment analysis for fan page ranking in Facebook (2014)
  44. The power of Twitter on predicting box office revenues (2012)
  45. The QWERTY effect: how typing shapes the meanings of words (2012)
  46. Towards automated personality identification using speech acts
  47. Topic and sentiment analysis on OSNs: a case study of advertising strategies on Twitter
  48. Trusting smartphone apps? To install or not to install, that is the question (2013)
  49. Tuned models of peer assessment in MOOCs (2013)
  50. Tweeting the meeting: an in-depth analysis of Twitter activity at Kidney Week 2011 (2012)
  51. Tweets are forever: a large-scale quantitative analysis of deleted tweets (2013)
  52. Twitter for public health: an open-source data solution
  53. US presidential election 2012 prediction using census corrected Twitter model (2012)
  54. Valence shifting: is it a valid task?
  55. Voices of victory: a computational focus group framework for tracking opinion shift in real time
  56. Whisper: tracing the spatiotemporal process of information diffusion in real time (2012)

[edit] Mentioning

  1. A novel sentiment analysis of social networks using supervised learning
  2. Automatic mood classification of Indonesian tweets using linguistic approach
  3. Crawling JavaScript websites using WebKit - with application to analysis of hate speech in online discussions
  4. Demystifying MapReduce
  5. GU-MLT-LT: Sentiment Analysis of Short Messages using Linguistic Features and Stochastic Gradient Descent
  6. Increasing the willingness to collaborate online: an analysis of sentiment-driven interactions in peer content production (2011, closed access)
  7. NTNU: domain semi-independent short message sentiment classification
  8. Privacy nudges for OSNs: a review (2014)
  9. SemEval-2013 task 2: sentiment analysis in Twitter
  10. SentiMeter-Br: Facebook and Twitter analysis tool to discover consumers' sentiment
  11. The muses of poetry - in search of the poetic experience
  12. Time-space varying visual analysis of micro-blog sentiment
  13. The next generation poetic experience
  14. The potential of microblogs for the study of public perceptions of climate change
  15. VADER: a parsimonious rule-based model for sentiment analysis of social media text
  16. Workshop on computational personality recognition: shared task

[edit] Student papers

  1. Análisis Estático y Dinámico de opiniones en Twitter (Felipe José Bravo Márquez, 2013)
  2. Analyzing emotion on Twitter for user modeling (Shaolong Li, Master Thesis, 2013)
  3. Classification and visualisation of Twitter sentiment data (mentioning, Mikael Brevik, Øyvind Selmer, 2013 Master Thesis)
  4. Distributional methods for sentiment analysis (Emmanuele Chersoni, 2013, Master Thesis)
  5. Evaluation of natural language processing techniques for sentiment analysis on tweets (2012, Bachelor Thesis)
  6. Exploitation of tweets to measure experienced utility (Allison Madigan, 2013)
  7. Natural language processing methods for attitudinal near-synonymy
  8. Opinion mining and name entity detection from news comments
  9. Retweets--but not just retweets: quantifying and predicting influence on Twitter (2012, Bachelor thesis)
  10. Why not! Sequence labeling the scope of negation using dependency features (2012 Master Thesis)
  11. Sentiment analysis of microblogs (Tobias Günther, 2013)

Andrew Ng's CS 229 class:

  1. Robert Chang, Sam Pimentel, Alexandr Svistunov, Sentiment Analysis of Occupy Wall Street Tweets, 2011. [1]
  2. Derek Farren, Predicting retail website anomalies using Twitter data, 2012. [2]

Mentioning:

  1. A framework to analyse and visualise public sentiment using Twitter data
  2. Sentiment analysis for Bangla microblog posts

[edit] Blogs

  1. Finn Årup Nielsen's blog, posts tagged with 'afinn'.
  2. Tracking US Sentiments Over Time In Wikileaks
  3. Kaushal Agrawal – Data Visualization – Mood of the Artist
  4. First shot: Sentiment Analysis in R, Andy Bromberg
  5. All Your Tweets Are Belong To Us: the Twitterverse declares a winner

[edit] Mentioning

  1. Sometimes I think we don’t deserve good data Google Ngram.
  2. Introduction to Sentiment Analysis , Carl Anderson, OneKingsLane.com

[edit] Tools

  1. Simplest sentiment analysis in Python with AFINN (note UNICODE issue for the word naïve, use "unicode(w, 'utf-8')" )
  2. AFINN-based sentiment analysis for Node.js
    1. application
  3. lexicons, Python and Javascript libraries
  4. AFINN-based sentiment analysis in Perl
  5. Afinn-for-Norsk
  6. Django
  7. Common Lisp
  8. troll, Javascript, Andrew Sliwinski.
  9. C-sharp (C#) by Tomasz Cielecki
  10. Große Gefühle Heise c't magazine in German with source code and tools in Ruby and Java.
  11. SAS Text Mining ("what's new" for the commercial product)

[edit] Services

[edit] Coursera

Bill Howe's Coursera course Introduction to Data Science has a sentiment analysis task were - apparently - AFINN is used on Twitter posts

[edit] References

  1. In search of reputation assessment: experiences with polarity classification in RepLab 2013
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