Comparative Analysis of Libraries for the Sentimental Analysis
Wendy Ccoya Edson Pinto
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.