Sentiment analysis tools in software engineering: A systematic mapping study

Obaidi, Martin; Nagel, Lukas; Specht, Alexander; Klünder, Jil

doi:10.1016/j.infsof.2022.107018

Cited by 16 publications

(8 citation statements)

References 34 publications

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“…Since it is impossible to manually keep up with the huge flow of new information appearing on the Internet, this sub-field (directly related to automatically extracting opinions) has significantly evolved in the last decade (Dhana Laxmi et al, 2020; Zhang et al, 2020). SA takes into account not only for investigating the positive or negative polarity of words and concepts, but also the syntactical tree of the sentence (Obaidi et al, 2022;Prager, 2006). The software makes an effort to analyse idiomatic and colloquial expressions, to provide meaning for negations, to change the polarity of words based on their proximity to other words (such as adverbs, adjectives, and conjunctions), and to account for certain functional-logic complements (Calefato et al, 2018;Hausmann et al, 2020).…”

Section: Study Background-sentiment Analysismentioning

confidence: 99%

Untitled

2023

IJSC

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Sentiment analysis has been rapidly employed for business decision support. New data mining researchers are yet to have an adequate understanding of the various applications of sentiment analysis while utilising social media data. As a result, it is critical to define the data mining and text analytics research trend holistically using existing literature. The study explores sentiment analysis research for its application in transforming social media data and identifies relevant research aspects through a comprehensive bibliometric review of 523 research articles published in the Scopus database (between 2018 and 2022) to discern the content and thematic analysis. Findings suggested that key purposes of the sentiment analysis are mainly related to innovation, transparency, and efficiency. Our review also highlights the distinctiveness of sentiment analysis for synthesising social media information to investigate various features, including the knowledge-domain map that detects author collaboration networks in the past.

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Section: Study Background-sentiment Analysismentioning

confidence: 99%

Untitled

2023

IJSC

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“…Since it is impossible to manually keep up with the huge ow of new information appearing on the Internet, the eld of automatically extracting opinions has signi cantly been evolving in the last decade (Dhana Laxmi et al, 2020;Zhang et al, 2020). SA takes into account not only the positive or negative polarity of words and concepts, but also the syntactical tree of the sentence (Obaidi et al, 2022;Prager, 2006). The software makes an effort to analyze idiomatic and colloquial expressions, to provide meaning for negations, to change the polarity of words based on their proximity to other words (such as adverbs, adjectives, and conjunctions), and to account for certain functional-logic complements (Calefato et al, 2018;Hausmann et al, 2020).…”

Section: 2 Sentiment Analysismentioning

confidence: 99%

A bibliometric analysis to explore sentiment analysis in the domain of social media research

Dahish¹,

Miah²

2022

Preprint

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Businesses use sentiment analysis in different ways for generating market or consumer insights to improve their performance. Existing sentiment analysis studies related to social media research have been significant due to the fact of their rapidly growing potential, as this type of analysis of social media content represents immense benefits. It is imperative to explore the need for such analysis to delineate the trend holistically for knowledge landscaping purposes. The study addresses the research gap through a comprehensive bibliometric review of 523 research articles published in the Scopus database (between 2018 and 2022) to discern the performance, content, and thematic analysis. We adopt an automated bibliometric study approach using the R-tool- biblioshiny for generating and presenting outcomes. Finding points on the vital usages of sentiment analysis such as innovation, transparency, and improved efficiency. It also highlights the uniqueness of sentiment analysis for synthesizing social media content to examine various aspects such as the knowledge-domain map that detects author collaboration networks as well as journal relationships.

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“…Software project management (SPM) process is crucial for effectively planning, executing, monitoring, and controlling software projects within specified constraints. In recent times, the application of Machine Learning (ML) and Deep Learning (DL) frameworks to support SPM has gained significant attention (Obaidi et al, 2022;Vusumuzi & Mfowabo, 2022;Sheoraj and Sungkur, 2022). Artificial intelligence (AI) is an emerging technology that has the potential to enhance SPM by providing intelligent and automated solutions for various SPM tasks (Mishra et al, 2023;Vusumuzi & Mfowabo, 2022; Karenkamp et al, 2020;Dam et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

“…A study by Obaidi et al (2022), it is highlighted the importance of understanding the impact and implications of ML and DL frameworks in different contexts and environments. The study envisaged that developing countries face unique challenges in SPM, such as limited resources, diverse stakeholders, cultural differences, and rapid changes.…”

Section: Introductionmentioning

confidence: 99%

The nature and practices of the use of Machine Learning and Deep Learning Frameworks to assist Software Project Management: A Developing Country Context

Nana Kwame Gyamfi

2024

jes

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The study conducted a comprehensive analysis of literature and interviews related to the use of ML and DL in SPM, identifying challenges and gaps in the field. The study adopts a qualitative research design, gathering data through semi-structured interviews with project managers involved in SPM using ML and DL frameworks. The collected data is analysed using thematic analysis to capture the participants' subjective experiences, perceptions, and opinions. The findings suggest that while the use of AI techniques for SPM has been gaining attention, it is still in its infancy, with limited research and practice in developing countries. The study also emphasizes the unique challenges faced by developing countries in SPM, including limited resources, diverse stakeholders, cultural differences, and rapid changes. It suggests that ML and DL frameworks can offer advantages in enhancing the productivity, innovation, and competitiveness of software projects in these contexts. However, the study acknowledges that there are limitations to these AI techniques for SPM, and further research is needed to address these limitations. The study bridge the gap between theory and practice by offering practical insights for project managers and recommendations for future research in utilizing ML and DL frameworks to assist SPM.

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Sentiment analysis tools in software engineering: A systematic mapping study

Cited by 16 publications

References 34 publications

Untitled

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A bibliometric analysis to explore sentiment analysis in the domain of social media research

The nature and practices of the use of Machine Learning and Deep Learning Frameworks to assist Software Project Management: A Developing Country Context

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