Comparative Analysis of Libraries for the Sentimental Analysis

• URL: http://arxiv.org/abs/2307.14311v1
• Date: Wed, 26 Jul 2023 17:21:53 GMT
• Title: Comparative Analysis of Libraries for the Sentimental Analysis
• Authors: Wendy Ccoya and Edson Pinto
• Abstract summary: This study is main goal is to provide a comparative comparison of libraries using machine learning methods. Five Python and R libraries NLTK, Textlob Vader, Transformers (GPT and BERT pretrained), and Tidytext will be used in the study to apply sentiment analysis techniques.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This study is main goal is to provide a comparative comparison of libraries using machine learning methods. Experts in natural language processing (NLP) are becoming more and more interested in sentiment analysis (SA) of text changes. The objective of employing NLP text analysis techniques is to recognize and categorize feelings related to twitter users utterances. In this examination, issues with SA and the libraries utilized are also looked at. provides a number of cooperative methods to classify emotional polarity. The Naive Bayes Classifier, Decision Tree Classifier, Maxent Classifier, Sklearn Classifier, Sklearn Classifier MultinomialNB, and other conjoint learning algorithms, according to recent research, are very effective. In the project will use Five Python and R libraries NLTK, TextBlob, Vader, Transformers (GPT and BERT pretrained), and Tidytext will be used in the study to apply sentiment analysis techniques. Four machine learning models Tree of Decisions (DT), Support Vector Machine (SVM), Naive Bayes (NB), and K-Nearest Neighbor (KNN) will also be used. To evaluate how well libraries for SA operate in the social network environment, comparative study was also carried out. The measures to assess the best algorithms in this experiment, which used a single data set for each method, were precision, recall, and F1 score. We conclude that the BERT transformer method with an Accuracy: 0.973 is recommended for sentiment analysis.

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