Optimization of Sentiment Analysis Using Teaching-Learning Based Algorithm

Abstract

Feature selection and sentiment analysis are two common studies that are currently being conducted; consistent with the advancements in computing and growing the use of social media. High dimensional or large feature sets is a key issue in sentiment analysis as it can decrease the accuracy of sentiment classification and make it difficult to obtain the optimal subset of the features. Furthermore, most reviews from social media carry a lot of noise and irrelevant information. Therefore, this study proposes a new text-feature selection method that uses a combination of rough set theory (RST) and teaching-learning based optimization (TLBO), which is known as RSTLBO. The framework to develop the proposed RSTLBO includes numerous stages:(1) acquiring the standard datasets (user reviews of six major U.S. airlines) which are used to validate search result feature selection methods, (2) preprocessing of the dataset using text processing methods. This involves applying text processing methods from natural language processing techniques, combined with linguistic processing techniques to produce high classification results, (3) employing the RSTLBO method, and (4) using the selected features from the previous process for sentiment classification using the Support Vector Machine (SVM) technique. Results show an improvement in sentiment analysis when combining natural language processing with linguistic processing for text processing. More importantly, the proposed RSTLBO feature selection algorithm is able to produce an improved sentiment analysis.

Multiclass feature selection with metaheuristic optimization algorithms: a review

“…The method fulfilled its objective of increasing performance, reducing the computational cost, increasing accuracy, removing irrelevant data, and helping faster model learning. Muhammad et al [ 167 ] presented a novel text feature selection technique that used an amalgamation of rough set theory (RST) and TLBO known as RSTLBO. They developed four frameworks in the RSTLBO: the acquisition of standard datasets, dataset pre-processing,utilizing the RSTLBO method; selected feature set used by employing the SVM technique.…”

Customer sentiment analysis for Arabic social media using a novel ensemble machine learning approach

“…An approach for analyzing sentiment is used for datasets gathered from Arabic social media, in which a corpus is manually built, as detailed in [10]. The outcomes of this research are reported in [11]. Several ML techniques are executed on the training and testing datasets to ascertain whether or not the suggested approach produces accurate results.…”

Enhanced Sentiment Analysis Algorithms for Multi-Weight Polarity Selection on Twitter Dataset

“…A set of machine learning algorithms are applied to the training and testing datasets to identify the accuracy of the proposed approach. As presented in [8], a sentiment analysis mechanism is proposed for processing sentiments from learning and teaching datasets. A feature selection is applied to the processed data by creating four models and a sentiment classification using a support vector machine (SVM) algorithm is implemented to provide the accuracy of the models.…”

“…Language Polarity /Weight Mechanism [7] Apply classification algorithms on a corpus approach for analyzing sentiments and terms. Arabic 3-Weight Supervised [8] Classify learning datasets using SVM algorithm. English 3-Weight Supervised [9] Classifying tweets using Naïve Bayes.…”