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Category: AFINN

Sentiment analysis

Papers: DOAJ Google Scholar PubMed
Ontologies: MeSH NeuroLex Wikidata Wikipedia
Other: Google Twitter WolframAlpha

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

The word lists are also distributed from within a Python program:

This Python package also contains a Danish word list.

The word list and its evaluation are described in:

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


[edit] Relationship to ANEW

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 words 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. Finn Årup Nielsen was in no way involved in development of ANEW. ANEW was developed by Margaret M. Bradley and Peter J. Lang.

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

[edit] Misconceptions

  • "AFINN can be considered as an expansion of ANEW [30], a dictionary created to provides emotional ratings for English words."[1] Actually not. ANEW was not used during the development of AFINN. There might be words in ANEW that is not in AFINN.
  • "It is important to mention that AFINN does not include any emoticons."[2] This is not entirely precise. Emoticons are available in the Python package AFINN available at Github and PYPI.

[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.[3]
  2. 0.72-0.77 accuracy in two-class polarity classification with Twitter corpora.[4]
  3. 0.65-0.79 accuracy in two-class subjectivity classification with Twitter corpora.[5]
  4. Performance on product reviews is reported in Data-driven integration of multiple sentiment dictionaries for lexicon-based sentiment classification of product reviews.
  5. 71% and 63% accuracy on two-class polarity classification on Twitter data.[6]
  6. 62.2% accuracy on two-class sentiment analysis of the movie review data set
  7. A comparative study on Twitter sentiment analysis: which features are good? 4 different datasets with AFINN as features in a machine learning classifier: 74-75.2% (SemEval dataset), around 70% (Sanders dataset), 58.7-60.5% (Health Care Reform dataset[7]) and around 62.5-62.8% (Obama-McCain Debate dataset[8]). In this study AFINN was shown to be one of the best features for Twitter sentiment analysis.
  8. How translation alters sentiment, part of an Arabic sentiment analysis system.
  9. Evaluating the effectiveness of hashtags as predictors of the sentiment of tweets shows accuracy results for classifying between sentiment and non-sentiment tweets.
  10. A comparative study of different sentiment lexica for sentiment analysis of tweets evaluates features extracted with AFINN on a data set from the SemEval competition.
  11. An evaluation of machine translation for multilingual sentence-level sentiment analysis evaluates AFINN together with a number of other sentiment analysis methods on multilingual datasets.
  12. Comprehensive study on lexicon-based ensemble classification sentiment analysis evaluates AFINN on various review data sets together with other sentiment analysis methods.
  13. A benchmark comparison of state-of-the-practice sentiment analysis methods is an elaborate evaluation of multiple sentiment analysis methods with multiple datasets. they write: "The top seven methods based on Macro-F1 are SentiStrength, Semantria, AFINN, OpinionLexicon, Umigon, Vader and SO-CAL. This means that these methods produce good results across several datasets for both, 2 and 3-class tasks. These methods would be preferable in situations in which any sort of preliminary evaluation would be performed." See also SentiBench - a benchmark comparison of state-of-the-practice sentiment analysis methods.
  14. Um benchmark para comparação de métodos para análise de sentimentos
  15. Using hashtags as labels for supervised learning of emotions in Twitter messages. Comparison with ANEW and LIWC based on hashtag labels

See also AFINN used with Italian translation in IRADABE: adapting english lexicons to the Italian sentiment polarity classification task.

Memory and CPU consumption is evaluated in Towards sentiment analysis for mobile devices.

[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 benchmark comparison of state-of-the-practice sentiment analysis methods
  3. A clustering analysis of tweet length and its relation to sentiment
  4. A comparative study on Twitter sentiment analysis: which features are good?
  5. A comparative study of different sentiment lexica for sentiment analysis of tweets
  6. A constrained multi-view clustering approach to influence role detection
  7. A data-centric framework for development and deployment of Internet of Things applications in clouds
  8. A design proto- pattern for continuously evaluated forecasting in IBM® InfoSphere® Streams
  9. A framework for knowledge derivation incorporating trust and quality of data (2013)
  10. A fine-grained sentiment analysis approach for detecting crisis related microposts (2013)
  11. A hybrid approach for emotion detection in support of affective interaction
  12. A longitudinal study of follow predictors on Twitter (2013)
  13. A novel transit rider satisfaction metric: rider sentiments measured from online social media data
  14. A rule-based approach to implicit emotion detection in text (2015)
  15. A simple and efficient algorithm for lexicon generation inspired by structural balance theory
  16. Acquiring knowledge of affective events from blogs using label propagation (2016)
  17. Aesthetic considerations for automated platformer design (2012)
  18. An empirical, quantitative analysis of the differences between sarcasm and irony (2016)
  19. An evaluation of machine translation for multilingual sentence-level sentiment analysis (2016)
  20. Analyzing and predicting news popularity on Twitter (2015)
  21. Annotate-Sample-Average (ASA): a new distant supervision approach for Twitter sentiment analysis
  22. Are emoticons good enough to train emotion classifiers of Arabic tweets?
  23. Aspect based sentiment analysis (2014, PhD Thesis)
  24. Audience targeting by B-to-B advertisement classification: a neural network approach
  25. Automated fictional ideation via knowledge base manipulation
  26. AUEB: two stage sentiment analysis of social network messages (2014, SemEval)
  27. AUEB-ABSA at SemEval-2016 Task 5: supervised machine learning for aspect based sentiment analysis
  28. Bias-aware lexicon-based sentiment analysis
  29. Bimodal feature-based fusion for real-time emotion recognition in a mobile context
  30. BiSAL - a bilingual sentiment analysis lexicon to analyze Dark Web forums for cyber security
  31. BOUNCE: sentiment classification in Twitter using rich feature sets (2013)
  32. Building a Twitter opinion lexicon from automatically-annotated tweets (2016)
  33. Building domain specific sentiment lexicons combining information from many sentiment lexicons and a domain specific corpus
  34. Building sentiment lexicons applying graph theory on information from three Norwegian thesauruses
  35. Can Twitter_Trends predict election results? Evidence from 2014 Indian general election
  36. Capturing place semantics from users' interaction on the geosocial web (2014)
  37. Catching fish in the stream: real time analysis of audience behavior in social media (2013)
  38. Character-to-character sentiment analysis in Shakespeare's plays (2013)
  39. CLaC-SentiPipe: SemEval2015 Subtasks 10 B,E, and Task 11
  40. Citius: a naive-Bayes strategy for sentiment analysis on English tweets (2014)
  41. Combining strengths, emotions and polarities for boosting Twitter sentiment analysis
  42. Combining textual pre-game reports and statistical data for predicting success in the National Hockey League (2014)
  43. Comprehensive study on lexicon-based ensemble classification sentiment analysis (2016)
  44. Constructing sentiment lexicons in Norwegian from a large text corpus
  45. Contextual sentiment analysis using conditional random fields
  46. Contradiction detection between opinions (2013)
  47. Cooperative, dynamic Twitter parsing and visualization for dark network analysis (2013)
  48. Crawling JavaScript websites using WebKit – with application to analysis of hate speech in online discussions (2013)
  49. Crowd sentiment detection during disasters and crises
  50. Cues to deception in social media communications
  51. Data-driven integration of multiple sentiment dictionaries for lexicon-based sentiment classification of product reviews
  52. Detecting predatory behaviour in game chats (2015)
  53. Detecting important Swedish innovations by text mining articles from the 1970s (2016)
  54. Determing health utilities through data mining of social media
  55. Determining the level of clients' dissatisfaction from their commentaries
  56. DigitalHealth: exploring users' perspectives through social media analysis (2015)
  57. dispel4py: an agile framework for data-intensive eScience
  58. Discovering content-based behavioral roles in social networks
  59. ELiRF: a support vector machine approach for sentiment analysis tasks in Twitter at SemEval-2015 (2015)
  60. Empirical study of relationship between Twitter mood and stock market from an Indian context (2015)
  61. Enhanced news-reading: interactive and visual integration of social media information
  62. Enhancing lexicon-based review classification by merging and revising sentiment dictionaries
  63. Ensemble learning methods for pay-per-click campaign management (2015)
  64. Ensemble learning techniques for structured and unstructured data (2015, thesis)
  65. Evaluating the effectiveness of hashtags as predictors of the sentiment of tweets
  66. Exploratory search on Twitter utilizing user feedback and multi-perspective microblog analysis
  67. Extracting sentiment networks from Shakespeare's plays
  68. Fast and accurate - improving lexicon-based sentiment classification with an ensemble methods (2016)
  69. FBM-Yahoo! at RepLab 2012 (2012)
  70. Figurative messages and affect in Twitter: Differences between irony, sarcasm and not
  71. Forex-Foreteller: currency trend modeling using news articles
  72. From opinion lexicons to sentiment classification of tweets and vice versa: a transfer learning approach
  73. From unlabelled tweets to Twitter-specific opinion words
  74. Full-FACE poetry generation
  75. Fuzzy subjective sentiment phrases: a context sensitive and self-maintaining sentiment lexicon (2014)
  76. Geo-spatial event detection in the Twitter stream
  77. GeoSRS: a hybrid social recommender system for geolocated data (2015)
  78. Good friends, bad news - affect and virality in Twitter (2011)
  79. GTI: an unsupervised approach for sentiment analysis in Twitter
  80. HBE: hashtag-based emotion lexicons for Twitter sentiment analysis
  81. How stock market reacts to budget announcement? Through the lens of social media in Indian context
  82. Identifying consumers' arguments in text (2012)
  83. Identifying disputed topics in the news (2014)
  84. iFeel 2.0: a multilingual benchmarking system for sentence-level sentiment analysis (2016)
  85. IIIT-H at SemEval 2015: Twitter sentiment analysis - the good, the bad and the neutral!
  86. IIT-TUDA at SemEval-2016 Task 5: beyond sentiment lexicon: combining domain dependency and distributional semantics features for aspect based sentiment analysis
  87. In search of reputation assessment: experiences with polarity classification in RepLab 2013 (2013)
  88. Inferring topic-dependent influence roles of Twitter users
  89. IOA: improving SVM based sentiment classification through post processing
  90. IRADABE: adapting english lexicons to the Italian sentiment polarity classification task
  91. KLUE: simple and robust methods for polarity classification
  92. KLUEless: polarity classification and association
  93. Knowledge-based tweet classification for disease sentiment monitoring (2016)
  94. Learning to recognize affective polarity in similes
  95. language-related information for the Linguistic Linked Data Cloud
  96. LLT-PolyU: identifying sentiment intensity in ironic tweets
  97. Machine reading for abstractive summarization of customer reviews in the touristic domain
  98. Making the most of tweet-inherent features for social spam detection on Twitter
  99. Measuring the influence of mass media on opinion segregation through Twitter (2014)
  100. Measuring NBA players mood by mining athlete-generated content (2015)
  101. Meta-level sentiment models for big social data analysis
  102. Mining Facebook data for predictive personality modeling
  103. Monitoring travel-related information on social media through sentiment analysis (2014)
  104. Movie script summarization as graph-based scene extraction
  105. Multi-domain collaboration for Web-based literature browsing and curation
  106. Networks and language in the 2010 election
  107. New lessions from the War in Bosnia
  108. NTNU: domain semi-independent short message sentiment classification
  109. On enhancing the label propagation algorithm for sentiment analysis using active learning with an artificial oracle
  110. Opinion retrieval in Twitter: is proximity effective?
  111. Opinion retrieval in Twitter using stylistic variations
  112. Organizing news articles and editorials through information extraction and sentiment analysis
  113. Performance evaluation of sentiment classification using query strategies in a pool based active learning scenario (2015)
  114. Positive, negative, or neutral: learning an expanded opinion lexicon from emoticon-annotated tweets
  115. Post-visit and pre-visit tourist destination image through eWOM sentiment analysis and perceived helpfulness
  116. Predictingg tie strength with the Facebook API (2014)
  117. Privacy nudges for social media: an exploratory Facebook study (2013)
  118. Probing of geospatial stream data to report disorientation (2013)
  119. Real-time monitoring of sentiment in business related Wikipedia (2013)
  120. Rule-based visual mappings -- with a case study on poetry visualization (2013)
  121. Representing and resolving negation for sentiment analysis (2012)
  122. Restaurant rating: industrial standard and word-of-mouth a text mining and multi-dimensional sentiment analysis
  123. SABRE: a sentiment aspect-based retrieval engine
  124. Sarcasm as contrast between a positive sentiment and negative situation (2013)
  125. Semi-automated argumentative analysis of online product reviews (2012)
  126. Sensing social media for corporate reputation management: a business agility perspective
  127. SentiBench-a benchmark comparison of state-of-the-practice sentiment analysis methods
  128. Sentiment analysis for telco popularity on Twitter big data using a novel malaysian dictionary
  129. Sentiment analysis of product attribute using social media
  130. Sentiment analysis on microblogs for natural disasters management: a study on the 2014 Genoa Floodings
  131. Sentiment classification of online political discussions: a comparison of a word-based and dependency-based method (2014)
  132. Sentiment valences for automatic personality detection of online social networks users using three factor model (2015)
  133. Sentiue: target and aspect based sentiment analysis in SemEval-2015 Task 12 (2015)
  134. Simple approaches of sentiment analysis via ensemble learning
  135. Social media data analysis using Hadoop
  136. Solving the online review puzzle
  137. Some remarks on the internal consistency of online consumer reviews (2013)
  138. Someone to talk to in Advances in affective and pleasurable design.
  139. Strategies of legitimacy through social media: the networked strategy (2015)
  140. Study of different algorithms in sentiment analysis and the existing issues
  141. Suicidal tendencies: the automatic classification of suicidal and non-suicidal lyricists using NLP (2013)
  142. Summarization of yes/no questions using a feature function model (2011)
  143. Temperature and temperament: evidence from a billion tweets (2015)
  144. Text normalization of code mix and sentiment analysis
  145. Topic-specific stylistic variations for opinion retrieval on Twitter
  146. The emergence of online community leadership
  147. The FloWr framework: automated flowchart construction, optimisation and alteration for creative systems (2014)
  148. The impact of affective verbal content on predicting personality impressions in YouTube videos
  149. The language of mental health problems in social media
  150. The lexicon-based sentiment analysis for fan page ranking in Facebook (2014)
  151. The painful tweet: text, sentiment, and community structure analyses of tweets pertaining to pain (2015)
  152. The power of Twitter on predicting box office revenues (2012)
  153. The QWERTY effect: how typing shapes the meanings of words (2012)
  154. The use of POS sequence for analyzing sentence pattern in Twitter sentiment analysis
  155. Topic-specific stylistic variations for opinion retrieval on Twitter (2016)
  156. Towards sentiment analysis for mobile devices
  157. Tracking sentiment by time series analysis
  158. Twista - an application for the analysis and visualization of tailored tweet collections
  159. TwitterHawk: a feature bucket approach to sentiment analysis (2015)
  160. Towards automated personality identification using speech acts
  161. Topic and sentiment analysis on OSNs: a case study of advertising strategies on Twitter
  162. Trusting smartphone apps? To install or not to install, that is the question (2013)
  163. Tuned models of peer assessment in MOOCs (2013)
  164. Tweeting the meeting: an in-depth analysis of Twitter activity at Kidney Week 2011 (2012)
  165. Tweets are forever: a large-scale quantitative analysis of deleted tweets (2013)
  166. Twitter for public health: an open-source data solution
  167. Twitter sentiment analysis on E-commerce websites in India
  168. Twitter sentiment classification on Sanders data using hybrid approach
  169. Twitter sentiment detection via ensemble classification using averaged confidence scores
  170. Two classifiers in arbiter tree to analyze data
  171. Uncovering patterns in student work: machine learning to understand human learning
  172. Understanding online hotel reviews through automated text analysis (2016)
  173. Unsupervised method for sentiment analysis in online texts (2016)
  174. US presidential election 2012 prediction using census corrected Twitter model (2012)
  175. Use of social media data to explore crisis informatics(?) (2015)
  176. Using combined lexical resources to identify hashtag types
  177. Using hashtags as labels for supervised learning of emotions in Twitter messages (2014)
  178. Using social media data to study people's perception and knowledge of environments
  179. Valence shifting: is it a valid task?
  180. ValenTo: sentiment analysis of figurative language tweets with irony and sarcasm
  181. Voices of victory: a computational focus group framework for tracking opinion shift in real time
  182. Webis: an ensemble for Twitter sentiment detection
  183. Whisper: tracing the spatiotemporal process of information diffusion in real time (2012)

[edit] Mentioning

  1. A machine learning attack against the civil rights CAPTCHA
  2. A mathematical approach to gauging influence by identifying shifts in the emotions of social media users
  3. A novel sentiment analysis of social networks using supervised learning
  4. An overview of sentiment analysis in social media and its applications in disaster relief
  5. A quantitative analysis of gender differences in movies using psycholinguistic normatives
  6. A survey on opinion mining and sentiment analysis: tasks, approaches and applications
  7. Adapting sentiment lexicons using contextual semantics for sentiment analysis of Twitter
  8. Advanced classification lists (dirty word lists) for automatic security classification
  9. An integrated text analytic framework for product defect discovery (2014)
  10. Automatic mood classification of Indonesian tweets using linguistic approach
  11. Benchmarking sentiment analysis methods for large-scale texts: a case for using continuum-scored words and word shift graphs
  12. Classifying emotion in Thai youtube comments
  13. Crawling JavaScript websites using WebKit - with application to analysis of hate speech in online discussions
  14. Comparative study: the implementation of machine learning method for sentiment analysis in social media. A recommendation for future research
  15. Demystifying MapReduce
  16. Detection and scoring of Internet slangs for sentiment analysis using SentiWordNet
  17. Differential emotions and the stock market - the case of company-specific trading
  18. Engagement with health agencies on Twitter
  19. Feature-level sentiment analysis applied to Brazilian Portuguese reviews
  20. Feature sentiment diversification of user generated reviews: the FREuD approach (2015)
  21. GU-MLT-LT: Sentiment Analysis of Short Messages using Linguistic Features and Stochastic Gradient Descent
  22. Increasing the willingness to collaborate online: an analysis of sentiment-driven interactions in peer content production (2011, closed access)
  23. Natural language processing in accounting, auditing and finance: a synthesis of the literature with a roadmap for future research
  24. NTNU: domain semi-independent short message sentiment classification
  25. Methodology for Twitter sentiment analysis
  26. Mining fine-grained argument elements
  27. Mining social network data for cyber physical system
  28. Opinion recognition on movie reviews by combining classifiers
  29. OpinionMining-ML
  30. Overview of the Evalita 2014 SENTIment POLarity Classification task
  31. Predicting personality traits of Chinese users based on Facebook wall posts (2015)
  32. Privacy nudges for OSNs: a review (2014)
  33. Product aspect ranking using sentiment analysis: a survey
  34. Reducing sparsity in sentiment analysis data using novel dimensionality reduction approaches
  35. Retrieving minority product reviews using positive/negative skewness
  36. Self-reflective sentiment analysis
  37. SemEval-2013 task 2: sentiment analysis in Twitter
  38. SemEval-2015 task 12: aspect based sentiment analysis
  39. SemEval-2016 Task 7: determining sentiment intensity of English and Arabic phrases
  40. Sentiment analysis over social networks: an overview
  41. Sentiment analysis: detecting valence, emotions, and other affectual states from text (Saif M. Mohammad review)
  42. Sentiment analysis techniques in recent works
  43. Sentiment resources: lexicons and datasets
  44. SentiMeter-Br: Facebook and Twitter analysis tool to discover consumers' sentiment
  45. Simultaneous feature selection and parameter optimization using multi-objective optimization for sentiment analysis
  46. Social data integration and analytics for health intelligence
  47. State of the art language technologies for Italian: the EVALITA 2014 perspective
  48. SWIMS: semi-supervised subjective feature weighting and intelligent model selection for sentiment analysis
  49. TASS 2015 - the evolution of the Spanish opinion mining systems
  50. Text mining of news-headlines for FOREX market prediction: a multi-layer dimension reduction algorithm with semantics and sentiment
  51. The challenges of sentiment detection in the social programmer ecosystem
  52. The comprehension of figurative language: what is the influence of irony and sarcasm on NLP techniques?
  53. The impact of sentiment-driven feedback on knowledge reuse in online communities
  54. The muses of poetry - in search of the poetic experience
  55. The sound of valence: phonological features predict word meaning
  56. Time-space varying visual analysis of micro-blog sentiment
  57. The next generation poetic experience
  58. The potential of microblogs for the study of public perceptions of climate change
  59. Universality and creativity: the usage of language in gender and irony
  60. VADER: a parsimonious rule-based model for sentiment analysis of social media text
  61. Workshop on computational personality recognition: shared task
  62. Создание лексикона оценочных слов русского языка РуСентиЛекс

[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. Aspect-based opinion summarization of product reviews on social media (Ásgeir Ögmundarson, Master Thesis, 2015)
  4. Classification and visualisation of Twitter sentiment data (mentioning, Mikael Brevik, Øyvind Selmer, 2013 Master Thesis)
  5. Combining lexicon- and learning-based approaches for improved performance and convenience in sentiment classification
  6. Distributional methods for sentiment analysis (Emmanuele Chersoni, 2013, Master Thesis)
  7. Evaluation of natural language processing techniques for sentiment analysis on tweets (2012, Bachelor Thesis)
  8. Exploitation of tweets to measure experienced utility (Allison Madigan, 2013)
  9. Forecasting success in the National Hockey League using in-game statistics and textual data (Master Thesis, 2014)
  10. Natural language processing methods for attitudinal near-synonymy
  11. Opinion mining and name entity detection from news comments
  12. Real-time Twitter sentiment classification based on Apache Storm (2015, Master Thesis)
  13. Relationships between social media sentiment, customer satisfaction, and stock price performance (Master Thesis, 2013)
  14. Retweets--but not just retweets: quantifying and predicting influence on Twitter (2012, Bachelor thesis)
  15. Sentiment mining using machine learning optimization (2015 Master Thesis)
  16. Social media sentiment analysis (2014 Master Thesis)
  17. Social media sentiment analysis for stock price behavior prediction
  18. Using movie screenplays as a barometer for mood in history (2014, [1])
  19. Twitter data analysis for financial markets
  20. TwitterCritic: sentiment analysis of tweets to predict TV ratings
  21. Um benchmark para comparação de métodos para análise de sentimentos (Master Thesis, 2015)
  22. Una aproximación basada en aprendizaje automático para diversos problemas de procesamiento de lenguaje natural en redes sociales (2016)
  23. Using social media content to inform agent-based models for humanitarian crisis response (Bachelor Thesis, 2014)
  24. Why not! Sequence labeling the scope of negation using dependency features (2012 Master Thesis)
  25. 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. [2]
  2. Derek Farren, Predicting retail website anomalies using Twitter data, 2012. [3]

[edit] Mentioning

  1. A framework to analyse and visualise public sentiment using Twitter data
  2. Análise de sentimentos em textos curtos provenientes de redes sociais
  3. Inference of personality using social media profiles
  4. Mining social media: methods and approaches for content analysis
  5. Sentiment analysis for Bangla microblog posts
  6. Sentiment analysis of Norwegian Twitter messages
  7. Sentiment mining using machine learning optimization
  8. Twitter sentiment analysis (specialization project, Fall 2015)

[edit] Other

  1. A Autofilter System for Online Social Network's IJESC of unknown quality.

[edit] Patent


[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
  6. The Happiest And Saddest Fans In Baseball, FiveThirtyEight, 2015.
  7. If I Loved Natural Language Processing Less, I Might Be Able to Talk About It More, sentiment analysis of Jane Austen works by Julia Silge.
  8. More on Normalizing Sentiment Distributions, John Laudun.

[edit] Mentioning

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

[edit] Tools

  1. Simplest sentiment analysis in Python with AFINN (note UNICODE issue for the word naïve, use "unicode(w, 'utf-8')" )
  2. Javascript
    1. AFINN-based sentiment analysis for Node.js
    2. application
    3. Retext sentiment
  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)
  12. A Better Place, chrome extension. The extension was made in response to Experimental evidence of massive-scale emotional contagion through social networks.
  13. syuzhet, R. [4]
  14. Data Science Toolkit, webservice that uses AFINN for online sentiment analysis

[edit] Language versions

[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. SentiBench - a benchmark comparison of state-of-the-practice sentiment analysis methods
  2. [[Annotate-Sample-Average (ASA): a new distant supervision approach for Twitter sentiment analysis]]
  3. In search of reputation assessment: experiences with polarity classification in RepLab 2013
  4. Meta-level sentiment models for big social data analysis
  5. Meta-level sentiment models for big social data analysis
  6. Fuzzy subjective sentiment phrases: a context sensitive and self-maintaining sentiment lexicon
  7. Twitter polarity classification with label propagation over lexical links and the follower graph
  8. Twitter polarity classification with label propagation over lexical links and the follower graph
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