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The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
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Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
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The Stereotype Content Model (SCM) was first proposed by Susan Fiske and her colleagues (Fiske, Cuddy, Glick & Xu, 2002; see also Fiske, 2012 and Fiske, 2017). The SCM specifies that when someone encounters a new group, they will stereotype them based on two metrics: warmth—or that group’s perceived intent, and how likely they are to provide help or inflict harm—and competence—or their ability to carry out that objective. Depending on the warmth-competence...
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The confidence interval is the range of values around the mean that contains the true mean. It is expressed as a probability percentage. The interpretation of a 95% confidence interval, for instance, is that the statistician is 95% confident that the true mean falls within the interval. The upper and lower limits of this range are known as confidence limits. The confidence limits for the true mean are estimated from the sample's mean, the standard deviation, and the statistical factor...
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A confidence interval is a better estimate of the population than a point estimate, as it uses a range of values from a sample instead of a single value.
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The Scalable Fuzzy Inference-Based Ensemble Method for Sentiment Analysis.

Yunus Emre Isikdemir1, Hasan Serhan Yavuz1

  • 1Eskisehir Osmangazi University, Electrical and Electronics Engineering Department, Eskisehir 26480, Turkey.

Computational Intelligence and Neuroscience
|October 10, 2022
PubMed
Summary

This study introduces a scalable sentiment analysis framework using fuzzy logic to combine multiple methods. The novel approach improves accuracy in classifying sentiments from large internet text datasets.

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Area of Science:

  • Natural Language Processing
  • Machine Learning
  • Data Science

Background:

  • The internet generates vast amounts of user-generated text data (social media, blogs, news).
  • Institutions need efficient methods to analyze this big data for public opinion and sentiment.
  • Existing sentiment analysis methods can be improved for accuracy and scalability.

Purpose of the Study:

  • To propose a scalable sentiment classification framework using a fuzzy inference mechanism.
  • To enhance sentiment analysis by ensembling diverse methods like dictionary-based, word embedding, and count vectorization.
  • To improve upon classical ensemble methods by incorporating weighted base learners and fuzzy rules.

Main Methods:

  • A fuzzy inference system was designed to evaluate compound probability scores from sentiment analysis techniques.
  • The system integrates valence-aware dictionaries, word embeddings, and count vectorization.
  • A novel ensemble approach allows weighting of base learners and combines algorithms using fuzzy rules.

Main Results:

  • The proposed framework was tested on four tagged social network datasets.
  • Experimental results demonstrated improved accuracy for both 2-class (positive/negative) and 3-class (positive/neutral/negative) sentiment classification.
  • The fuzzy inference mechanism effectively combined strengths of different sentiment analysis algorithms.

Conclusions:

  • The developed scalable framework offers improved accuracy for sentiment classification.
  • The fuzzy ensemble approach provides a robust method for analyzing large volumes of text data.
  • This research contributes to more effective big data analysis for understanding public opinion.