CoChat
Sign Up
Cited by 219Sentiment Analysis and Opinion MiningText and Document Classification TechnologiesHate Speech and Cyberbullying Detection

On Stopwords, Filtering and Data Sparsity for Sentiment Analysis of Twitter

Open access
Saif, Hassan, Fernandez, Miriam, He, Yulan, Alani, Harith·2014·Proceedings of the Language Resources and Evaluation Conference
DOI 10.63317/3uepwmfe264a
Open this paper in CoChat — free accountOpen PDF

Abstract

Sentiment classification over Twitter is usually affected by the noisy nature (abbreviations, irregular forms) of tweets data.A popular procedure to reduce the noise of textual data is to remove stopwords by using pre-compiled stopword lists or more sophisticated methods for dynamic stopword identification.However, the effectiveness of removing stopwords in the context of Twitter sentiment classification has been debated in the last few years.In this paper we investigate whether removing stopwords helps or hampers the effectiveness of Twitter sentiment classification methods.To this end, we apply six different stopword identification methods to Twitter data from six different datasets and observe how removing stopwords affects two well-known supervised sentiment classification methods.We assess the impact of removing stopwords by observing fluctuations on the level of data sparsity, the size of the classifier's feature space and its classification performance.Our results show that using pre-compiled lists of stopwords negatively impacts the performance of Twitter sentiment classification approaches.On the other hand, the dynamic generation of stopword lists, by removing those infrequent terms appearing only once in the corpus, appears to be the optimal method to maintaining a high classification performance while reducing the data sparsity and substantially shrinking the feature space.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.63317/3uepwmfe264a

Cites

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • Removal of functional words and stopwords may embed useful signals about authorship and/or emotional states, threatening research validity.

    “Even if such words might not be useful for certain analyses, for other applications they may embed useful signals about e.g., authorship and/or emotional states (Pennebaker et al., 2003; Saif et al., 2014), threatening as a result the research validity (Denny and Spirling, 2016).”

    From Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries (2019) · cites this paper as Saif et al., 2014

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

No references could be resolved yet.

Research works printed but not linked: 29

  1. Asiaee T, A., Tepper, M., Banerjee, A., and Sapiro, G. (2012). If you are happy and you know it... tweet. In Proceedings of the 21st ACM international conference on Information and knowledge management, pages 1602- 1606. ACM.
  2. Ayral, H. and Yavuz, S. (2011). An automated domain spe- cific stop word generation method for natural language text classification. In Innovations in Intelligent Systems and Applications (INISTA), 2011 International Sympo- sium on, pages 500-503. IEEE.
  3. Bakliwal, A., Arora, P., Madhappan, S., Kapre, N., Singh, M., and Varma, V. (2012). Mining sentiments from tweets. Proceedings of the WASSA, 12.
  4. Cover, T. M. and Thomas, J. A. (2012). Elements of infor- mation theory. John Wiley & Sons.
  5. Forman, G. (2003). An extensive empirical study of feature selection metrics for text classification. The Journal of machine learning research, 3:1289-1305.
  6. Fox, C. (1992). Information retrieval data structures and algorithms. Lexical Analysis and Stoplists, pages 102- 130.
  7. Gokulakrishnan, B., Priyanthan, P., Ragavan, T., Prasath, N., and Perera, A. (2012). Opinion mining and senti- ment analysis on a twitter data stream. In Advances in ICT for Emerging Regions (ICTer), 2012 International Conference on, pages 182-188. IEEE.
  8. Hu, X., Tang, J., Gao, H., and Liu, H. (2013a). Unsuper- vised sentiment analysis with emotional signals. In Pro- ceedings of the 22nd international conference on World Wide Web, pages 607-618. International World Wide Web Conferences Steering Committee.
  9. Hu, X., Tang, L., Tang, J., and Liu, H. (2013b). Exploiting social relations for sentiment analysis in microblogging. In Proceedings of the sixth ACM international conference on Web search and data mining, pages 537-546. ACM.
  10. Kouloumpis, E., Wilson, T., and Moore, J. (2011). Twitter sentiment analysis: The good the bad and the omg! In Proceedings of the ICWSM, Barcelona, Spain.
  11. Lo, R. T.-W., He, B., and Ounis, I. (2005). Automatically building a stopword list for an information retrieval sys- tem. In Journal on Digital Information Management: Special Issue on the 5th Dutch-Belgian Information Re- trieval Workshop (DIR).
  12. Luhn, H. P. (1957). A statistical approach to mechanized encoding and searching of literary information. IBM Jour- nal of research and development, 1(4):309-317.
  13. Makrehchi, M. and Kamel, M. S. (2008). Automatic ex- traction of domain-specific stopwords from labeled docu- ments. In Advances in information retrieval, pages 222- 233. Springer.
  14. Martınez-Cámara, E., Montejo-Ráez, A., Martın-Valdivia, M., and Urena-López, L. (2013). Sinai: Machine learn- ing and emotion of the crowd for sentiment analysis in mi- croblogs. In In Proceedings of the seventh international workshop on Semantic Evaluation Exercises (SemEval- 2013), Atlanta, Georgia, USA, June 2013.
  15. Nakov, P., Rosenthal, S., Kozareva, Z., Stoyanov, V., Ritter, A., and Wilson, T. (2013). Semeval-2013 task 2: Sen- timent analysis in twitter. In In Proceedings of the 7th International Workshop on Semantic Evaluation. Associa- tion for Computational Linguistics.
  16. Pak, A. and Paroubek, P. (2010). Twitter as a corpus for sentiment analysis and opinion mining. In Proceedings of LREC 2010, Valletta, Malta.
  17. Phan, X.-H., Nguyen, L.-M., and Horiguchi, S. (2008). Learning to classify short and sparse text & web with hidden topics from large-scale data collections. In Pro- ceedings of the 17th international conference on World Wide Web, pages 91-100. ACM.
  18. Saif, H., He, Y., and Alani, H. (2012a). Alleviating data sparsity for twitter sentiment analysis. In Proceed- ings, 2nd Workshop on Making Sense of Microposts (#MSM2012) in conjunction with WWW 2012, Layon, France.
  19. Saif, H., He, Y., and Alani, H. (2012b). Semantic sentiment analysis of twitter. In Proceedings of the 11th interna- tional conference on The Semantic Web, Boston, MA.
  20. Saif, H., Fernandez, M., He, Y., and Alani, H. (2013). Eval- uation datasets for twitter sentiment analysis. In Proceed- ings, 1st Workshop on Emotion and Sentiment in Social and Expressive Media (ESSEM) in conjunction with AI*IA Conference, Turin, Italy.
  21. Shamma, D., Kennedy, L., and Churchill, E. (2009). Tweet the debates: understanding community annotation of un- collected sources. In Proceedings of the first SIGMM workshop on Social media, pages 3-10. ACM.
  22. Silva, C. and Ribeiro, B. (2003). The importance of stop word removal on recall values in text categorization. In Neural Networks, 2003. Proceedings of the International Joint Conference on, volume 3, pages 1661-1666. IEEE.
  23. Sinka, M. P. and Corne, D. (2003a). Evolving better sto- plists for document clustering and web intelligence. In HIS, pages 1015-1023.
  24. Sinka, M. P. and Corne, D. W. (2003b). Towards mod- ernised and web-specific stoplists for web document anal- ysis. In Web Intelligence, 2003. WI 2003. Proceedings. IEEE/WIC International Conference on. IEEE.
  25. Speriosu, M., Sudan, N., Upadhyay, S., and Baldridge, J. (2011). Twitter polarity classification with label propa- gation over lexical links and the follower graph. In Pro- ceedings of the EMNLP First workshop on Unsupervised Learning in NLP, Edinburgh, Scotland.
  26. Trumbach, C. C. and Payne, D. (2007). Identifying syn- onymous concepts in preparation for technology mining. Journal of Information Science, 33(6):660-677.
  27. Xu, Y., Jones, G. J., Li, J., Wang, B., and Sun, C. (2007). A study on mutual information-based feature selection for text categorization. Journal of Computational Informa- tion Systems, 3(3):1007-1012.
  28. Yang, Y. (1995). Noise reduction in a statistical approach to text categorization. In Proceedings of the 18th an- nual international ACM SIGIR conference on Research and development in information retrieval, pages 256-263. ACM.
  29. Zhang, L., Jia, Y., Zhou, B., and Han, Y. (2012). Microblog- ging sentiment analysis using emotional vector. In Cloud and Green Computing (CGC), 2012 Second International Conference on, pages 430-433. IEEE.

Cited by

Sorted by citations

Authors
Saif, H., Fernandez, M., He, Y., & Alani, H.
Affiliations
  • The Open University (GB)
  • Aston University (GB)
ORCID
Venue
Proceedings of the Language Resources and Evaluation Conference
Publisher
European Language Resources Association (ELRA)
Published
2014-05
Pages
810-817
ISSN
2522-2686
DOI
10.63317/3uepwmfe264a
OpenAlex
W2251345040
Cited by
219
References
0
Citations by year
2026: 6 · 2025: 1 · 2024: 15 · 2023: 21 · 2022: 18 · 2021: 32 · 2020: 36 · 2019: 23 · 2018: 29 · 2017: 17 · 2016: 12 · 2015: 6 · 2014: 3
FWCI
7.30
Type
conference paper
Language
en
Open access
Licence
cc-by-nc
Locations
  • Language Resources and Evaluation Conference (LREC) (publishedVersion, cc-by-nc)
  • Open Research Online (The Open University) (acceptedVersion)
  • Aston Publications Explorer (Aston University) (submittedVersion)
  • Language Resources and Evaluation
Funders
European Commission (611242)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Sentiment Analysis and Opinion Mining
  • Physical Sciences › Computer Science › Artificial Intelligence › Text and Document Classification Technologies
  • Physical Sciences › Computer Science › Artificial Intelligence › Hate Speech and Cyberbullying Detection
Keywords
stop word removal, Twitter sentiment analysis, data sparsity, feature space reduction, abbreviations

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review