Automatic classification of software related microblogs
IEEE International Conference on Software Maintenance, ICSM, Page: 596-599
2012
- 46Citations
- 239Usage
- 25Captures
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
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Metrics Details
- Citations46
- Citation Indexes46
- 46
- CrossRef20
- Usage239
- Downloads185
- Abstract Views54
- Captures25
- Readers25
- 25
Conference Paper Description
Millions of people, including those in the software engineering communities have turned to microblogging services, such as Twitter, as a means to quickly disseminate information. A number of past studies by Treude et al., Storey, and Yuan et al. have shown that a wealth of interesting information is stored in these microblogs. However, microblogs also contain a large amount of noisy content that are less relevant to software developers in engineering software systems. In this work, we perform a preliminary study to investigate the feasibility of automatic classification of microblogs into two categories: relevant and irrelevant to engineering software systems. We extract features from the textual content of the microblogs and the titles of any URLs mentioned in the microblogs. These features are then used to learn a discriminative model used in classifying relevant and irrelevant microblogs. We show that our trained model can achieve a promising classification performance. © 2012 IEEE.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=84873174991&origin=inward; http://dx.doi.org/10.1109/icsm.2012.6405330; http://ieeexplore.ieee.org/document/6405330/; http://xplorestaging.ieee.org/ielx5/6384336/6404866/06405330.pdf?arnumber=6405330; https://ink.library.smu.edu.sg/sis_research/1576; https://ink.library.smu.edu.sg/cgi/viewcontent.cgi?article=2575&context=sis_research; http://ink.library.smu.edu.sg/sis_research/1576; http://ink.library.smu.edu.sg/cgi/viewcontent.cgi?article=2575&context=sis_research
Institute of Electrical and Electronics Engineers (IEEE)
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