Machine Learning Approaches to Sentiment Analysis in Linguistic
Abstract
In the present era of techs and machines, various machine learning models including Support Vector Machines (SVM), Naive Bayes, Decision Trees, and Neural Networks are examined in detail, highlighting their strengths, weaknesses, and suitability for sentiment analysis tasks. The objective of this study is to assess the effectiveness of different machine learning techniques in capturing nuances of sentiment expressed in linguistic texts. It is aimed to examine the performance of these approaches in handling challenges such as sarcasm, irony, and context-dependent sentiment. Through a comprehensive review and analysis of existing literature, the goal is to identify key research gaps and propose potential avenues for future research. In data procedure the extraction of textual data from websites, forums, social media platforms, and other online sources is known as data scraping. In data analysis performance evaluation to assessed the effectiveness of the model, compute a number of evaluation measures, including recall, accuracy, precision, F1-score, and confusion matrix. Analysed the model's predictions qualitatively by looking at samples that were incorrectly classified and detecting any biases or tendencies. In the results strong open-source NLP system BERT (Bidirectional Encoder Representations from Transformers) is excellent at deciphering context and confusing words. The findings show that the selected method had a considerable impact on how well sentiment analysis models performed. The findings contribute to the advancement of sentiment analysis methodologies tailored specifically for linguistic data.
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