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metadata
license: mit
tags:
  - infinite-dataset-hub
  - synthetic

GlobalSearchEnginesFeatures

tags: feature extraction, regression, sentiment analysis

Note: This is an AI-generated dataset so its content may be inaccurate or false

Dataset Description:

The 'GlobalSearchEnginesFeatures' dataset compiles various attributes and user feedback metrics from multiple leading search engines worldwide. The dataset is designed for machine learning applications in feature extraction and sentiment analysis, focusing on aspects that influence search engine performance and user satisfaction.

CSV Content Preview:

index,SearchEngine,UserInterface,MobileFriendly,SEOfriendly,AlgorithmTransparency,UserSatisfaction,EngagementScore,RelevanceRating,FeedbackSentiment
1,"Google","High","Yes","High","Medium","Very High","A","4.5","Positive"
2,"Bing","Medium","Yes","Medium","Low","High","B","4.0","Neutral"
3,"Yahoo","Low","No","Low","High","Medium","C","3.5","Negative"
4,"DuckDuckGo","Medium","Yes","High","High","High","A","4.8","Very Positive"
5,"Baidu","High","Yes","Medium","Medium","Medium","A","4.2","Positive"

The dataset features a comprehensive list of popular search engines along with qualitative and quantitative data. The Labels column (named UserSatisfaction, EngagementScore, and FeedbackSentiment) provides categorical data representing the overall user satisfaction and sentiment analysis from user feedback, while the AlgorithmTransparency offers insight into how transparent the search engine is about its ranking algorithms. This information can be utilized in regression analysis to predict trends in user satisfaction or in developing more user-centric search engines.

Source of the data:

The dataset was generated using the Infinite Dataset Hub and microsoft/Phi-3-mini-4k-instruct using the query 'list of search engines ':