Tweets Classification on the Base of Sentiments for US Airline Companies

Abstract

The use of data from social networks such as Twitter has been increased during the last few years to improve political campaigns, quality of products and services, sentiment analysis, etc. Tweets classification based on user sentiments is a collaborative and important task for many organizations. This paper proposes a voting classifier (VC) to help sentiment analysis for such organizations. The VC is based on logistic regression (LR) and stochastic gradient descent classifier (SGDC) and uses a soft voting mechanism to make the final prediction. Tweets were classified into positive, negative and neutral classes based on the sentiments they contain. In addition, a variety of machine learning classifiers were evaluated using accuracy, precision, recall and F1 score as the performance metrics. The impact of feature extraction techniques, including term frequency (TF), term frequency-inverse document frequency (TF-IDF), and word2vec, on classification accuracy was investigated as well. Moreover, the performance of a deep long short-term memory (LSTM) network was analyzed on the selected dataset. The results show that the proposed VC performs better than that of other classifiers. The VC is able to achieve an accuracy of 0.789, and 0.791 with TF and TF-IDF feature extraction, respectively. The results demonstrate that ensemble classifiers achieve higher accuracy than non-ensemble classifiers. Experiments further proved that the performance of machine learning classifiers is better when TF-IDF is used as the feature extraction method. Word2vec feature extraction performs worse than TF and TF-IDF feature extraction. The LSTM achieves a lower accuracy than machine learning classifiers.

GBSVM: Sentiment Classification from Unstructured Reviews Using Ensemble Classifier

“…Three features have been investigated as well including TF, TF-IDF, and word2vec. The performance of VC in [9] is better with TF and TF-IDF which yields an accuracy of 78.9%, and 79.1%, respectively. The approach in [9] is tested on the selected dataset with TF-IDF uni-gram features and it achieves an accuracy of 88.01%.…”

“…The performance of VC in [9] is better with TF and TF-IDF which yields an accuracy of 78.9%, and 79.1%, respectively. The approach in [9] is tested on the selected dataset with TF-IDF uni-gram features and it achieves an accuracy of 88.01%. On the other hand, the accuracy of the proposed GBSVM is 93.0% which is much better than that of [9].…”

“…Specifically, the machine learning models show variant performance when used with a specific pre-processing strategy and particular feature selection method. Voting classifiers proved to show superior performance than single classification models, as in [9], where a voting classifier based on LR and Stochastic Gradient Descent Classifier (SGDC) is used for tweet classification. As a consequence, this research aims at devising a Voting Classifier (VC) which can perform better than already proposed models to predict sentiments polarity from unstructured text.…”

“…A performance comparison of the proposed GBSVM is done as well to show its performance against other similar models. Authors in [9] perform tweet classification based on user sentiments with a VC which is based on LR and SGDC. Three features have been investigated as well including TF, TF-IDF, and word2vec.…”

“…Research works [3,7] report superior performance of uni-and bi-gram features in sentiment classification. Even so, a dedicated classifier is not suitable for tweets and often a combination also called voting or ensemble of multiple classifiers proves to show excellent performance for sentiment analysis [9]. This paper first investigates the performance of various machine learning supervised models and then contrive a voting classifier to perform sentiment analysis on a Twitter dataset.…”