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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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An Improvised Sentiment Analysis Model on Twitter Data Using Stochastic Gradient Descent (SGD) Optimization Algorithm

K P Vidyashree1, A B Rajendra1

  • 1Department of Information Science and Engineering, Vidyavardhaka College of Engineering, Mysuru, India.

SN Computer Science
|February 7, 2023
PubMed
Summary

This study introduces an improved sentiment analysis model for Twitter data. The proposed stochastic gradient descent optimization algorithm based on stochastic gradient neural network (SGDOA-SGNN) outperforms existing models in classifying tweet sentiment.

Keywords:
Improvised modelStochastic gate neural networkStochastic gradient descentText dataTweetsTwitter API

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Area of Science:

  • Natural Language Processing
  • Machine Learning
  • Data Mining

Background:

  • Sentiment analysis extracts opinions from unstructured text data like product and movie reviews.
  • It is crucial for market analysis, brand monitoring, and understanding public opinion.
  • Analyzing Twitter data for sentiment is a growing area in natural language processing.

Purpose of the Study:

  • To propose an improvised sentiment analysis model for classifying tweet polarity (positive, neutral, negative).
  • To evaluate the performance of the proposed model against existing methods.

Main Methods:

  • Dataset gathered using the Twitter API and Twitter package.
  • Utilized stochastic gradient descent (SGD) algorithm with a stochastic gradient neural network (SGNN).
  • Developed a novel stochastic gradient descent optimization Algorithm based on stochastic gradient neural network (SGDOA-SGNN).

Main Results:

  • The SGDOA-SGNN model demonstrated superior performance compared to the Forest-Whale Optimization Algorithm based on deep neural network (F-WOA-DNN) model.
  • Accurate categorization of tweet sentiments into positive, neutral, and negative classes was achieved.

Conclusions:

  • The proposed SGDOA-SGNN model offers an effective approach for sentiment analysis of Twitter data.
  • This advancement contributes to more accurate public opinion mining and social media analysis.