Comparative study on sentimental analysis using machine learning techniques

Abstract

With the advancement of the Internet and the world wide web (WWW), it is observed that there is an exponential growth of data and information across the internet. In addition, there is a huge growth in digital or textual data generation. This is because users post the reply comments in social media websites based on the experiences about an event or product. Furthermore, people are interested to know whether the majority of potential buyers will have a positive or negative experience on the event or the product. This kind of classification in general can be attained through Sentiment Analysis which inputs unstructured text comments about the product reviews, events, etc., from all the reviews or comments posted by users. This further classifies the data into different categories namely positive, negative or neutral opinions. Sentiment analysis can be performed by different machine learning models like CNN, Naive Bayes, Decision Tree, XgBoost, Logistic Regression etc. The proposed work is compared with the existing solutions in terms of different performance metrics and XgBoost outperforms out of all other methods.

Liver Disease Prediction and Classification using Machine Learning Techniques

“…With this, it can be inferred that LR is an appropriate strategy for disease prediction. A novel feature model with the classification methods Random Forest, SVM, J48, Bayesian Net, and MLP was proposed in [13], [14], [15].…”

A Hybrid Deep Learning Framework for Efficient Sentiment Analysis

“…We previously covered sentiment analysis using machine learning techniques [21] on similar datasets, as well as data cleaning and feature extraction, and this project serves as an expansion, digging into sentiment analysis using deep learning. Our findings reveal that machine learning models like Random Forest models demonstrate competitive performance, especially in scenarios with limited data.…”