article · PLoS ONE
Feature extraction plays a vital role in sentiment classification by drawing meaningful information out of raw text data. To identify the most effective methods for enhancing sentiment analysis, several feature extraction techniques were evaluated and compared from a machine learning perspective. The examined approaches included Bag-of-words, Word2Vector, N-gram, Term Frequency-Inverse Document Frequency (TF-IDF), Hashing Vectorizer, and Global Vectors for Word Representation (GloVe). These techniques were applied to two distinct text sources: the Twitter US airlines dataset and an Amazon musical instrument reviews dataset. A random forest classifier was trained using a seventy-thirty train-test split to measure and contrast their effectiveness across different performance metrics. Among all tested options, the TF-IDF method delivered the highest performance, achieving ninety-nine percent accuracy on the Amazon reviews and ninety-six percent accuracy on the Twitter data.
Automated sentiment analysis helps organisations make sense of vast amounts of public opinion and customer feedback. Knowing which text processing methods deliver the highest accuracy allows practitioners to build more dependable tools for interpreting reviews and social media commentary without spending unnecessary computational effort on less effective feature extraction techniques.
This applied and tested research provides guidance for developers building sentiment analysis systems to monitor customer sentiment from online reviews or social media. By demonstrating that TF-IDF yields high accuracy with a standard random forest classifier, the findings can be directly integrated into commercial text analysis pipelines. However, the work remains at the benchmark stage using historical public datasets rather than a live, deployed product.
AI-generated from the published abstract. Always read the original work before citing.
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.
This page summarises published work. The authoritative version sits with the publisher.
DOI: 10.1371/journal.pone.0294968
Is something wrong with this record? Report it or request removal.
Discussion
Have you built on this work, tried to replicate it, or seen it applied in practice? Share what you know. Verified researchers and MARATTO™ domain experts can open a discussion, and any member can reply. Contributions are reviewed before they appear.
No discussion yet. Open the first thread.
New to MARATTO™? Create a free account.
