Sentiment Analysis

Twitter-sentiment analysis of Moroccan diabetic using Fuzzy C-means SMOTE and deep neural network

Effectively managing diabetes as a lifestyle condition involves fostering awareness, and social media is a powerful tool for this purpose.  Analyzing the content of tweets on platforms like Twitter can greatly inform health communication strategies aimed at raising awareness about diabetes within the Moroccan community.  Unfortunately, the corpus of tweets is imbalanced and the feature extraction leads to data sets with a very high dimension which affects the quality of sentiment analysis.  This study focused on analyzing the content, sentiment, and reach of tweets spec

DEVELOPING A SENTIMENT ANALYZER USING CHATGPT FOR A STOCK MARKET

Today, an important problem of financial successis to find effective trading approaches that can adapt to rapidly changing market conditions and ensure high investment returns. Based on the literature analysis, ChatGPT is identified as a promising technology that is more effective than FinBert in being used as a component for conducting sentiment analysis of stocks. The research also shows satisfactory efficiency and productivity of ChatGPT.

Information system of feedback monitoring in social networks for the formation of recommendations for the purchase of goods

This paper describes an information system for monitoring and analyzing reviews on social networks to form recommendations for the purchase of goods. This system is designed to be used by customers to speed up and facilitate the search for the necessary products on e-commerce resources. Successful selection of a quality product according to the desired criteria is extremely important, as it saves search time and customer money. Analyzing comments on the network, the information system recommends the product if there is a preponderance of positive feedback on it.

Towards a polynomial approximation of support vector machine accuracy applied to Arabic tweet sentiment analysis

Machine learning algorithms have become very frequently used in natural language processing, notably sentiment analysis, which helps determine the general feeling carried within a text.  Among these algorithms, Support Vector Machines have proven powerful classifiers especially in such a task, when their performance is assessed through accuracy score and f1-score.  However, they remain slow in terms of training, thus making exhaustive grid-search experimentations very time-consuming.  In this paper, we present an observed pattern in SVM's accuracy, and f1-score approximated with a Lagrange

Визначення типів емоційного мовного висловлювання у додатках автоматичного опрацювання текстів

The paper deals with the means of expression of emotions in the language of the Internet. The existing methods of extraction of emotional elements in the text, as well as systems that implement the analysis of the emotional elements were analyzed in the article. Features of emotional elements are described, by means of which they are defined in the text. The use of the notation of Backus-Naur form to highlight the emotional elements was proposed in the paper. The proposed approach was implemented using the Java programming language.