![]() Additionally, significant differences were observed in most comparisons when examining the statistical difference between the weighted F1-score of TSC and other classifiers using a Wilcoxon signed-ranks test. The highest ROC-PRC (Area under the Precision–Recall Curve) score was obtained in one dataset and comparable in four other datasets. The highest AUC-ROC (Area under the Receiver Operating Characteristics) score was obtained in two datasets and comparable in six other datasets. Results from transformer-based vectors demonstrate that TSC outperforms five well-known machine learning algorithms on four datasets, and it is comparable with all other datasets based on the weighted F1, Precision and Recall scores. Both pre-trained SBERT and TF-IDF vectors were used in the experimental analysis. Experiments were performed on ten well-researched datasets which include both short and long text. The proposed TSC algorithm takes advantage of the recent advances in efficient feature extraction and vector generation from pre-trained bidirectional transformer encoders for creating tolerance classes. Near sets theory is a more recent soft computing methodology inspired by rough sets where instead of set approximation operators used by rough sets to induce tolerance classes, the tolerance classes are directly induced from the feature vectors using a tolerance level parameter and a distance function. The TSC algorithm is a supervised learning method based on tolerance near sets. In this paper, we present a soft computing technique-based algorithm (TSC) to classify sentiment polarities of tweets as well as news categories from text. Some applications of text classification include sentiment classification and news categorization. Text classification aims to assign labels to textual units such as documents, sentences and paragraphs. Therefore, this work proposes a method for the analysis of sentiment in social networks in such a way that it adapts to the needs of organizations or sectors, and the acceptance or rejection of the population can be efficiently identified from what is exposed in a social network. Sentiment analysis, for organizations and social groups, has become a necessity that must be covered to identify the acceptance of an idea or its management. Here, the objective is to determine the feelings of the population toward a brand, a product, or a service and to even identify the reactions of people to events and trends generated in their environment. On the contrary, emerging technologies and social networks have become non-traditional sources that provide large volumes of data that can be exploited using different data analysis methods. ![]() However, data generation is not limited to traditional sources. For this reason, data analysis has become one of the fastest-growing technologies when it comes to generating information and knowledge about data generated by organizations. ![]() The main contributions of this survey include the presentation of the proposed approaches for sentiment analysis in Twitter, their categorization according to the technique they use, and the discussion of recent research trends of the topic and its related fields.Äecision making is vital for the management of all organizations. Resources that have been used in the Twitter sentiment analysis literature are also briefly presented. In addition, we discuss fields related to sentiment analysis in Twitter including Twitter opinion retrieval, tracking sentiments over time, irony detection, emotion detection, and tweet sentiment quantification, tasks that have recently attracted increasing attention. The presented studies are categorized according to the approach they follow. This survey provides an overview of the topic by investigating and briefly describing the algorithms that have been proposed for sentiment analysis in Twitter. Sentiment analysis in Twitter tackles the problem of analyzing the tweets in terms of the opinion they express. Twitter is one of themost popular microblog platforms on which users can publish their thoughts and opinions. Sentiment analysis in Twitter is a field that has recently attracted research interest.
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