Twitter Sentiment Analysis Using Naive Bayes-Based Machine Learning Technique

Gaur, Priya; Vashistha, Sudhanshu; Jha, Pradeep J

doi:10.1007/978-981-19-5443-6_27

Cited by 12 publications

(1 citation statement)

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“…Gaur et al . [ 17 ] used a machine learning algorithm based on the NB Classifier with TF-IDF feature extraction to classify the Twitter sentiment 140 dataset. Based on performance metrics including precision, recall, and accuracy, the suggested model’s results showed improved accuracy (84.44%) and precision.…”

Section: Background Literaturementioning

confidence: 99%

Enhancing machine learning-based sentiment analysis through feature extraction techniques

A. Semary,

Ahmed,

Amin

et al. 2024

PLoS ONE

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A crucial part of sentiment classification is featuring extraction because it involves extracting valuable information from text data, which affects the model’s performance. The goal of this paper is to help in selecting a suitable feature extraction method to enhance the performance of sentiment analysis tasks. In order to provide directions for future machine learning and feature extraction research, it is important to analyze and summarize feature extraction techniques methodically from a machine learning standpoint. There are several methods under consideration, including Bag-of-words (BOW), Word2Vector, N-gram, Term Frequency- Inverse Document Frequency (TF-IDF), Hashing Vectorizer (HV), and Global vector for word representation (GloVe). To prove the ability of each feature extractor, we applied it to the Twitter US airlines and Amazon musical instrument reviews datasets. Finally, we trained a random forest classifier using 70% of the training data and 30% of the testing data, enabling us to evaluate and compare the performance using different metrics. Based on our results, we find that the TD-IDF technique demonstrates superior performance, with an accuracy of 99% in the Amazon reviews dataset and 96% in the Twitter US airlines dataset. This study underscores the paramount significance of feature extraction in sentiment analysis, endowing pragmatic insights to elevate model performance and steer future research pursuits.

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Section: Background Literaturementioning

confidence: 99%