Sentiment Analysis on Top Five Cloud Service Providers in the Market

Koneru, Anupriya; Bhavani, Nerella Bala Naga Sai Rajani; Rao, K. Purushottama; Prakash, Garikipati Sai; Kumar, Immadisetty Pavan; Kumar, Velimala Venkat

doi:10.1109/icoei.2018.8553970

Cited by 3 publications

(3 citation statements)

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“…Developers can check Twitter for user reviews or opinions by searching for hashtags related to cloud products or services. It may also be helpful to check technology websites or forums for more in-depth reviews of cloud products [16] [18]. To summarise, ongoing research investigates technical texts using either lexicon-based or machine-learning methods, often employing technical data and specialised lexicon dictionaries as their foundation.…”

Section: Related Workmentioning

confidence: 99%

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BertSentiment-SO Model: Extracting Cloud Service Information from Stack Overflow’s Technical Texts

Sundaram,

Shou,

Subramaniam

2024

Proceedings of the 1st International Conference on Artificial Intelligence, Communication, IoT, Data Engineering and Security,

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The recent surge in sentiment analysis within software engineering texts has gained prominence due to its proven influence on productivity and collaborative work quality. Stack Overflow, a substantial repository of software engineering content, has become a focal point for sentiment analysis research, particularly in software development. Nevertheless, integrating sentiment analysis into software engineering has unveiled potential shortcomings in their efficacy. Researchers have had to adapt these tools to navigate limitations, as exemplified by the cases of SentiCR and Senti4SD. Moreover, natural language processing has made significant strides in recent times, primarily attributed to the emergence of pre-trained transformer-based models. Addressing this research gap, the study introduces a dedicated sentiment analysis model, BertSentiment-SO. This study delves explicitly into sentiment analysis for the three foremost cloud service providers: Amazon, Google, and Microsoft. Extracted from the Big Query public dataset, the data undergoes preprocessing and labelling via a semi-supervised learning approach. Harnessing the capabilities of BERT, our proposed model outperforms its counterparts, enabling a more precise score of 94.06% for the evaluation of sentiment within the domain of cloud services.

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Section: Related Workmentioning

confidence: 99%

“…This shortcoming has led to suboptimal performance in this domain. Several studies indicate the presence of sentiments within cloud service providers' discussions on Twitter [16] [17] [18]. However, no existing model or research studies explore sentiment within technical text among cloud service providers.…”

Section: Introductionmentioning

confidence: 99%

BertSentiment-SO Model: Extracting Cloud Service Information from Stack Overflow’s Technical Texts

Sundaram,

Shou,

Subramaniam

2024

Proceedings of the 1st International Conference on Artificial Intelligence, Communication, IoT, Data Engineering and Security,

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show abstract

“…To handle sentiment classification task, we can use available commercial cloud services tools. Some cloud services that have been employed by other researchers are Amazon Web Service [16,17], IBM Cloud [16], Google Cloud Platform [16] and Microsoft Text Analytics [16,18,19]. However, Arijit and William in [18] found that Microsoft Text Analytics was 10-20% better in identifying positive and negative sentiment when evaluated in various tweet datasets.…”

Section: Introductionmentioning

confidence: 99%

Sentiment Analysis for Customer Review: Case Study of GO-JEK Expansion

Prananda

Thalib

2020

JISEBI

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Background: Market prediction is an important thing that needs to be analyzed deeply. Business intelligence becomes an important analysis procedure for analyzing the market demand and satisfaction. Since business intelligence needs a deep analysis, sentiment analysis becomes a powerful algorithm for analyzing customer review regarding to the business intelligence analysis.Objective: In this study, we perform a sentiment analysis for identifying the business intelligence analysis in GO-JEK.Methods: We use Twitter posts collected from the Twint library which consists of 3111 tweets. Since the dataset did not provide a ground truth, we perform Microsoft Text Analytic for determining positive, neutral, and negative sentiment. Before applying Microsoft Text Analytic, we conduct a pre-processing step to remove the unwanted data such as duplicate tweets, image, website address, etc.Results: According to the Microsoft Text Analytic, the results are 666 positive sentiment numbers, 2055 neutral sentiment numbers, and 127 negative sentiment numbers.Conclusion: According to these results, we conclude that most GO-JEK customers are satisfied with the GO-JEK services. In this research, we also develop classification model to predict the sentiment analysis of new data. We use some classifier algorithms such as Decision Tree, Naïve Bayes, Support Vector Machine and Neural Network. In the result, the system shows that the decision tree provides the best performance.

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Research on the Current Situation and Formal Evolution and Innovation Path of Film and Television Criticism Supported by Cloud Platforms

Xie

2024

Applied Mathematics and Nonlinear Sciences

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With the development of information technology and network communication, the film and television industry in the new era is rising and growing. In the film and television industry, film and television reviews are an indispensable part of the chain and play an important role in influencing the performance of film and television works to a great extent. In this paper, the ATCNN-SVM model is constructed to obtain sentiment classification results for film and television reviews and improve the generalization ability of the original model. Crawling the film and television reviews on relevant film websites, this text data is subjected to dimensional mining and dimensional correlation analysis, and the overall sentiment tendency of film and television reviews is obtained through sentiment-weighted accumulation. After a series of clustering and fusion steps, seven categories of film elements and five polarity sentiment scores were obtained for each film. When the sentiment score is -13, the frequency of films appearing is at most 2477, and at this time, film reviews mainly focus on filming techniques. Based on the results of the experimental analysis, we summarise the problems of film and television reviews at the present stage, and from a diversified perspective, we make positive and active reviews of domestic and non-domestic films, respectively, and analyse the innovative path of film and television review form evolution.

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Sentiment Analysis on Top Five Cloud Service Providers in the Market

Cited by 3 publications

References 10 publications

BertSentiment-SO Model: Extracting Cloud Service Information from Stack Overflow’s Technical Texts

BertSentiment-SO Model: Extracting Cloud Service Information from Stack Overflow’s Technical Texts

Sentiment Analysis for Customer Review: Case Study of GO-JEK Expansion

Research on the Current Situation and Formal Evolution and Innovation Path of Film and Television Criticism Supported by Cloud Platforms

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