Sentiment strength detection in short informal text

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Sentiment strength detection in short informal text
Authors: Mike Thelwall, Kevan Buckley, Georgios Paltoglou, Di Cai, Arvid Kappas
Citation: Journal of the American Society for Information Science and Technology 61 (12): 2544-2558. 2010 December
Database(s): Google Scholar cites
DOI: 10.1002/asi.21416.
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Article: BASE Google Scholar PubMed
Restricted: DTU Digital Library
Other: NIF
Format: BibTeX

Sentiment in short strength detection informal text (the title should have been Sentiment strength detection in short informal text: there is an errata to the text.) reports the development of a sentiment analysis system for estimating the sentiments in short texts (MySpace comments). The authors call the system SentiStrength.

The researchers set 5 human coders to label MySpace comments for sentiment. Positive and negative sentiment was labeled independently on two 5 point scales.


  1. Word list
  2. Score adjustment
  3. "miss" word
  4. Spelling correction
  5. Booster words
  6. Negation
  7. Spelling boosting
  8. Emoticon
  9. Exclamation
  10. Questions

Furthermore these features were considered:

  1. Phrase identification
  2. Semantic disambiguation

The SentiStrength algorithm was compared with "a range of standard machine-learning classification algorithms in Weka (Witten & Frank, 2005) using the frequencies of each word in the sentiment word list as the feature set." (page 2550).

[edit] Results

  • They found a Pearson correlation coeffients on 0.639-0.664 for the agreement between 3 human coders of sentiment strength on 1,041 MySpace comments.

[edit] Related studies

  1. A new ANEW: evaluation of a word list for sentiment analysis in microblogs
  2. Micro-blogging sentiment detection by collaborative online learning
  3. Recognizing contextual polarity: an exploration of features for phrase-level sentiment analysis also considers short text sentiment analysis
  4. Robust sentiment detection on Twitter from biased and noisy data
  5. Sentiment in Twitter events is a newer study by the first author, where SentiStrength is used for Twitter sentiment analysis.
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