Sentiment analysis of open source software community mailing list

Alesinloye, Jumoke Abass; Groarke, Eoin; Babu, Jaganath; Srinivasan, Subathra; Curran, Greg; Dennehy, Denis

doi:10.1145/3306446.3340824

Cited by 3 publications

(2 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The tweets were downloaded using the Twitter API and pre-processed in R. The study found that the maximum negative sentiment was from tweets of school libraries. Alesinloye et al (2019) analyzed the sentiments of conversation shared on Data Plane Development Kit (DPDK.org) mailing list. They have used the NLTK library for the analysis.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Sentiment analysis of online discussion of LIS professionals using R

Garg

Kanjilal

2022

LHTN

View full text Add to dashboard Cite

Purpose The main focus of the present study was threefold. Firstly, extraction of the data from the Library and Information Science (LIS) Links; secondly, pre-process the data to remove noises on maximum parameters; and lastly, to know the polarity of the discussion posted on LIS Links. Design/methodology/approach The advancement in the internet and Web 2.0 technologies have facilitated the academic community with many online platforms like Q&A sites, mailing lists, discussion forums, etc. for disseminating or seeking information. LIS Links is one such discussion forum commonly used by Indian LIS professionals. The analysis of these discussions can help in knowing the experience of LIS professionals in different aspects. The present study is based on a lexicon-based approach, which works on the bag of word model. The open source environment R and its different packages were used for the analysis. Findings The analysis shows that the majority of the posts on LIS Links were discussed with positive sentiments. There were only very few words of negative sentiments on the discussion forum of LIS Links. Originality/value The above review of the literature shows that there have been many studies in different domains. The user responses on different platforms were explored. Also, some studies have conducted sentiment analysis in LIS. However, this was limited to content posted by the libraries or the reviews of books. No research was found in the published literature on the analysis of the opinion of LIS professional.

show abstract

Section: Related Workmentioning

confidence: 99%

“…Alesinloye et al (2019) analyzed the sentiments of conversation shared on Data Plane Development Kit (DPDK.org) mailing list. They have used the NLTK library for the analysis.…”

Section: Introductionmentioning

confidence: 99%

Sentiment analysis of online discussion of LIS professionals using R

Garg

Kanjilal

2022

LHTN

View full text Add to dashboard Cite

show abstract

Exploration of Document-Oriented Stores' Capacities for Managing Community e-Mailing Lists: Elastic Stack as a Case Study

Moussa,

Ouechtatti,

Rezgui

et al. 2023

2023 International Symposium on Networks, Computers and Communications (ISNCC)

View full text Add to dashboard Cite

Opinion Mining for Software Development: A Systematic Literature Review

Lin

Cassee

Serebrenik

et al. 2022

ACM Trans. Softw. Eng. Methodol.

View full text Add to dashboard Cite

Opinion mining, sometimes referred to as sentiment analysis, has gained increasing attention in software engineering (SE) studies. SE researchers have applied opinion mining techniques in various contexts, such as identifying developers’ emotions expressed in code comments and extracting users’ critics toward mobile apps. Given the large amount of relevant studies available, it can take considerable time for researchers and developers to figure out which approaches they can adopt in their own studies and what perils these approaches entail. We conducted a systematic literature review involving 185 papers. More specifically, we present (1) well-defined categories of opinion mining-related software development activities, (2) available opinion mining approaches, whether they are evaluated when adopted in other studies, and how their performance is compared, (3) available datasets for performance evaluation and tool customization, and (4) concerns or limitations SE researchers might need to take into account when applying/customizing these opinion mining techniques. The results of our study serve as references to choose suitable opinion mining tools for software development activities and provide critical insights for the further development of opinion mining techniques in the SE domain.

show abstract

Sentiment analysis of open source software community mailing list

Cited by 3 publications

References 4 publications

Sentiment analysis of online discussion of LIS professionals using R

Sentiment analysis of online discussion of LIS professionals using R

Exploration of Document-Oriented Stores' Capacities for Managing Community e-Mailing Lists: Elastic Stack as a Case Study

Opinion Mining for Software Development: A Systematic Literature Review

Contact Info

Product

Resources

About