Sentiment Analysis in Airline Tweets Using Mutual Information for Feature Selection

Turdybay

et al. 2022

PeerJ Computer Science

Nowadays, people get increasingly attached to social media to connect with other people, to study, and to work. The presented article uses Twitter posts to better understand public opinion regarding the vegan (plant-based) diet that has traditionally been portrayed negatively on social media. However, in recent years, studies on health benefits, COVID-19, and global warming have increased the awareness of plant-based diets. The study employs a dataset derived from a collection of vegan-related tweets and uses a sentiment analysis technique for identifying the emotions represented in them. The purpose of sentiment analysis is to determine whether a piece of text (tweet in our case) conveys a negative or positive viewpoint. We use the mutual information approach to perform feature selection in this study. We chose this method because it is suitable for mining the complicated features from vegan tweets and extracting users’ feelings and emotions. The results revealed that the vegan diet is becoming more popular and is currently framed more positively than in previous years. However, the emotions of fear were mostly strong throughout the period, which is in sharp contrast to other types of emotions. Our findings place new information in the public domain, which has significant implications. The article provides evidence that the vegan trend is growing and new insights into the key emotions associated with this growth from 2010 to 2022. By gaining a deeper understanding of the public perception of veganism, medical experts can create appropriate health programs and encourage more people to stick to a healthy vegan diet. These results can be used to devise appropriate government action plans to promote healthy veganism and reduce the associated emotion of fear.

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Sentiment analysis of vegan related tweets using mutual information for feature selection

Turdybay

et al. 2022

PeerJ Computer Science

“…x A 0 j = {0.9770, 0.9719, 0.9513, 0.9597, 0.9770, 0.9770, 0.9766, 2.0112, 0.9646} After the normalization of the matrix X is obtained, the next step is to perform the weighted normalization by multiplying the criteria weight by the normalized weighted matrix according to the formula (16). The results of weighted normalization are presented in detail in Table 6.…”

Section: Evaluation Feature Selection Methods Using Arasmentioning

confidence: 99%

“…The proposed model achieved 97.1% accuracy compared to 92.0% on the comparison model. Next, Utama [16] performed feature selection using the mutual information (MI) model to predict the airline's tweet sentiment analysis. The feature selection made contributions to the classifier improvement.…”

Section: Related Workmentioning

confidence: 99%

Evaluation of Feature Selection Methods on Psychosocial Education Data Using Additive Ratio Assessment

et al. 2021

Artificial intelligence, particularly machine learning, is the fastest-growing research trend in educational fields. Machine learning shows an impressive performance in many prediction models, including psychosocial education. The capability of machine learning to discover hidden patterns in large datasets encourages researchers to invent data with high-dimensional features. In contrast, not all features are needed by machine learning, and in many cases, high-dimensional features decrease the performance of machine learning. The feature selection method is one of the appropriate approaches to reducing the features to ensure machine learning works efficiently. Various selection methods have been proposed, but research to determine the essential subset feature in psychosocial education has not been established thus far. This research investigated and proposed methods to determine the best feature selection method in the domain of psychosocial education. We used a multi-criteria decision system (MCDM) approach with Additive Ratio Assessment (ARAS) to rank seven feature selection methods. The proposed model evaluated the best feature selection method using nine criteria from the performance metrics provided by machine learning. The experimental results showed that the ARAS is promising for evaluating and recommending the best feature selection method for psychosocial education data using the teacher’s psychosocial risk levels dataset.

“…e grid search method with 10-fold CV has been used to find the machine learning algorithms' optimal hyperparameters and enhance the accuracy. Four machine learning classification algorithms, logistic regression (LR) [39], decision tree (DT) [40], random forest classifier (RF) [41,42], and support vector machine [43], are used in this work. e accuracy of cross-validation and unseen data is calculated for each model.…”

Section: Classifiers' Optimization and Trainingmentioning

confidence: 99%

Breast Cancer Identification from Patients’ Tweet Streaming Using Machine Learning Solution on Spark

et al. 2021

Twitter integrates with streaming data technologies and machine learning to add new value to healthcare. This paper presented a real-time system to predict breast cancer based on streaming patient’s health data from Twitter. The proposed system consists of two major components: developing an offline building model and an online prediction pipeline. For the first component, we made a correlation between the features to determine the correlation between features and reduce the number of features from the Breast Cancer Wisconsin Diagnostic dataset. Two feature selection algorithms are recursive feature elimination and univariate feature selection algorithms which are applied to features after correlation to select the essential features. Four decision trees, logistic regression, support vector machine, and random forest classifier have been used on features after correlation and feature selection. Also, hyperparameter tuning and cross-validation have been applied with machine learning to optimize models and enhance accuracy. Apache Spark, Apache Kafka, and Twitter Streaming API are used to develop the second component. The best model with the highest accuracy obtained from the first component predicts breast cancer in real time from tweets’ streaming. The results showed that the best model is the random forest classifier which achieved the best accuracy.