Open source software adoption evaluation through feature level sentiment analysis using Twitter data

Ikram, Muhammad Touseef; Butt, Naveed Anwer; Afzal, Muhammad

doi:10.3906/elk-1502-173

Cited by 7 publications

(3 citation statements)

References 19 publications

(20 reference statements)

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“…The goal of such studies is to forecast the sentiment of Bitcoin-related tweets, which could influence the cryptocurrency's market behaviour, as well as providing insights into how the public reacts to the adoption of Bitcoin, and how it affects the perception of the adopting companies. Ikram et al [27] investigate how potential adopters perceive the specific features of open-source software by examining the sentiments expressed on Twitter (San Francisco, CA, USA).…”

Section: Related Workmentioning

confidence: 99%

A Scalable and Automated Framework for Tracking the Likely Adoption of Emerging Technologies

Williams,

Anthi,

Burnap

2024

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While new technologies are expected to revolutionise and become game-changers in improving the efficiency and practices of our daily lives, it is also critical to investigate and understand the barriers and opportunities faced by their adopters. Such findings can serve as an additional feature in the decisionmaking process when analysing the risks, costs, and benefits of adopting an emerging technology in a particular setting. Although several studies have attempted to perform such investigations, these approaches adopt a qualitative data collection methodology, which is limited in terms of the size of the targeted participant group and is associated with a significant manual overhead when transcribing and inferring results. This paper presents a scalable and automated framework for tracking the likely adoption and/or rejection of new technologies from a large landscape of adopters. In particular, a large corpus of social media texts containing references to emerging technologies was compiled. Text mining techniques were applied to extract the sentiments expressed towards technology aspects. In the context of the problem definition herein, we hypothesise that the expression of positive sentiment implies an increase in the likelihood of impacting a technology user’s acceptance to adopt, integrate, and/or use the technology, and negative sentiment implies an increase in the likelihood of impacting the rejection of emerging technologies by adopters. To quantitatively test our hypothesis, a ground truth analysis was performed to validate that the sentiments captured by the text mining approach were comparable to the results provided by human annotators when asked to label whether such texts positively or negatively impact their outlook towards adopting an emerging technology. The collected annotations demonstrated comparable results to those of the text mining approach, illustrating that the automatically extracted sentiments expressed towards technologies are useful features in understanding the landscape faced by technology adopters, as well as serving as an important decisionmaking component when, for example, recognising shifts in user behaviours, new demands, and emerging uncertainties.

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Section: Related Workmentioning

confidence: 99%

A Scalable and Automated Framework for Tracking the Likely Adoption of Emerging Technologies

Williams,

Anthi,

Burnap

2024

Information

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“…Classification with a feature selection strategy demonstrates incomparable accuracy. Several techniques were widely used to extract opinions and sentiments from various blogs and are proposed in [11]. In [12], probabilistic latent semantic analysis and latent Dirichlet allocation were considered in analyzing topic distributions, which were based on variables ranging from topic words to documents.…”

Section: Survey On Opinion Mining Analysismentioning

confidence: 99%

Extended correlated principal component analysis with SVM-PUK in opinion mining

Devi¹,

Edara²,

Sistla³

et al. 2018

Turk J Elec Eng & Comp Sci

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With the rapid growth of microblogs and online sites, an inordinate number of product reviews are available on the Internet. They not only help in analyzing, but also assist in making informed decisions about product quality. In the proposed work, an extended correlated principal component analysis (ECPCA) is used for dimensionality reduction. A comparative analysis is conducted on movie reviews (DB-1) and Twitter datasets (DB-2 and DB-3) in opinion mining extraction. The performance of naïve Bayes, CHIRP, and support vector machine (SVM) with kernel methods such as radial basis function (RBF), polynomial, and Pearson (PUK) are compared and analyzed on the three datasets. The experimental results using ECPCA for selecting relevant features and SVM-PUK as a classifier exhibit better performance on movie reviews and Twitter datasets. The performance of the proposed approach is 99.69%, 99.4%, and 99.54% on the DB-1, DB-2, and DB-3 datasets, respectively, and comparatively outperforms the existing methods.

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“…Most contemporary studies have harnessed bigrams and trigrams [19][20][21][22]. However, the presence of technical and lexical terms in the citation context creates an extreme challenge for citation sentiment identification, which can be addressed with higher order n-grams that assist in capturing short-term contextual and positional information [23][24][25]. Therefore, in the proposed technique, we assume that higher order n-gram phrases, part of speech (POS) tagging, dependency relations, bag-of-words (BOW) models, and sentiment lexicons can play significant roles in improving classification accuracy.…”

Section: Introductionmentioning

confidence: 99%

Automated citation sentiment analysis using high order n-grams: a preliminary investigation

Ikram¹,

Afzal²,

Butt³

2018

Turk J Elec Eng & Comp Sci

Self Cite

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Abstract:Scientific papers hold an association with previous research contributions (i.e. books, journals or conference papers, and web resources) in the form of citations. Citations are deemed as a link or relatedness of the previous work to the cited work. The nature of the cited material could be supportive (positive), contrastive (negative), or objective (neutral). Extraction of the author's sentiment towards the cited scientific articles is an emerging research discipline due to various linguistic differences between the citation sentences and other domains of sentiment analysis. In this paper, we propose a technique for the identification of the sentiment of the citing author towards the cited paper by extracting unigram, bigram, trigram, and pentagram adjective and adverb patterns from the citation text. After POS tagging of the citation text, we use the sentence parser for the extraction of linguistic features comprising adjectives, adverbs, and n-grams from the citation text. A sentiment score is then assigned to distinguish them as positive, negative, and neutral.In addition, the proposed technique is compared with manually classified citation text and 2 commercial tools, namely SEMANTRIA and THEYSAY, to determine their applicability to the citation corpus. These tools are based on different techniques for determining the sentiment orientation of the sentence. Analysis of the results shows that our proposed approach has achieved results comparable to the commercial counterparts with average precision, recall, and accuracy of 90%, 81.82%, and 85.91% respectively.

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Open source software adoption evaluation through feature level sentiment analysis using Twitter data

Cited by 7 publications

References 19 publications

A Scalable and Automated Framework for Tracking the Likely Adoption of Emerging Technologies

A Scalable and Automated Framework for Tracking the Likely Adoption of Emerging Technologies

Extended correlated principal component analysis with SVM-PUK in opinion mining

Automated citation sentiment analysis using high order n-grams: a preliminary investigation

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