citation: @article{dataset_en_sentistrength,
      author = {Thelwall, Mike and Buckley, Kevan and Paltoglou, Georgios},
      title = {Sentiment Strength Detection for the Social Web},
      year = {2012},
      issue_date = {January 2012},
      publisher = {John Wiley \& Sons, Inc.},
      address = {USA},
      volume = {63},
      number = {1},
      issn = {1532-2882},
      url = {https://doi.org/10.1002/asi.21662},
      doi = {10.1002/asi.21662},
      abstract = {Sentiment analysis is concerned with the automatic extraction of sentiment-related
      information from text. Although most sentiment analysis addresses commercial tass,
      such as extracting opinions from product reviews, there is increasing interest in
      the affective dimension of the social web, and Twitter in particular. Most sentiment
      analysis algorithms are not ideally suited to this task because they exploit indirect
      indicators of sentiment that can reflect genre or topic instead. Hence, such algorithms
      used to process social web texts can identify spurious sentiment patterns caused by
      topics rather than affective phenomena. This article assesses an improved version
      of the algorithm SentiStrength for sentiment strength detection across the social
      web that primarily uses direct indications of sentiment. The results from six diverse
      social web data sets (MySpace, Twitter, YouTube, Digg, RunnersWorld, BBCForums) indicate
      that SentiStrength 2 is successful in the sense of performing better than a baseline
      approach for all data sets in both supervised and unsupervised cases. SentiStrength
      is not always better than machine-learning approaches that exploit indirect indicators
      of sentiment, however, and is particularly weaker for positive sentiment in news-related
      discussions. Overall, the results suggest that, even unsupervised, SentiStrength is
      robust enough to be applied to a wide variety of different social web contexts.},
      journal = {J. Am. Soc. Inf. Sci. Technol.},
      month = jan,
      pages = {163–173},
      numpages = {11}
  }
  