“Ayyappa Y., Siva Kumar A.P.”,Optimized long short-term memory-based stock price prediction with sentiment score,2023,“Sentiment analysis examines the emotional content of a statement, such as views, assessments, feelings, or attitudes about a topic, human, or object. Emotions can be categorized as either unbiased, good, or bad. It determines how people feel about the company online through social media. Based on the sentiments, the problem of solving the stock price prediction model is advantageous as it involves the sentiment score evaluated from the text information. This work introduces a new stock price prediction considering sentiment scores from text info in this concern. For that, we have considered news data and stock data. Moreover, this work falls under bigdata perspective by increasing the data size. The proposed model includes two major steps: feature extraction and prediction. Feature extraction takes place under two scenarios: features from news data and features from stock data. Features like Bag of words, n-Gram, TFIDF, and Improved cosine similarity are extracted from the news data, and features like improved exponential moving average and other existing technical indicator-based features such as ATR, TR are extracted from stock data. Both the feature sets are fused to determine the final prediction results. Particularly, this final observation involves the sentiments from the given news data. For this, optimized LSTM model is used, where the optimal training process will be carried out by a new Harris Hawks Induced Sparrow Search Optimization via tuning the optimal weights. The proposed model is the combination of Harris Hawks Optimization Algorithm and Sparrow Search Algorithm, respectively. Finally, the performance of proposed work will be evaluated over the other conventional models with respect to different measures. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Austria, part of Springer Nature.”,Classification; HHO; LSTM; Optimization; Sentiment; SSA; Stock prediction
“Mohamed O., Kassem A.M., Ashraf A., Jamal S., Mohamed E.H.”,An ensemble transformer-based model for Arabic sentiment analysis,2023,“Sentiment analysis is a common and challenging task in natural language processing (NLP). It is a widely studied area of research; it facilitates capturing public opinions about a topic, product, or service. There is much research that tackles English sentiment analysis. However, the research in the Arabic language is behind other high-resource languages. Recently, models such as bidirectional encoder representations from transformers (BERT) and generative pre-trained transformer (GPT) have been widely used in many NLP tasks; it significantly improved performance in NLP tasks, especially sentiment analysis. However, Arabic was not a priority in their development. Several models focusing on Arabic have recently begun to pave the way for the latest technologies, such as ARBERT, MARBERT, and others. We used multiple datasets for training and testing-ASAD-A Twitter-based Benchmark Arabic Sentiment Analysis Dataset, ArSarcasm-v2, and SemEval-2017. We propose an ensemble learning approach that combines the multilingual model(XLM-T) and the monolingual model(MARBERT) to overcome the intricacies of the Arabic language that are difficult to address with a single model. It also addresses the problem of imbalanced data using a combination of focal loss and label smoothing. The experiments showed that our ensemble learning approach outperforms the state-of-the-art models on all the used datasets. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Austria, part of Springer Nature.”,Arabic text; BERT; Ensemble learning; NLP; Sentiment analysis; Transformers
“Roy B., Das S.”,“Perceptible sentiment analysis of students’ WhatsApp group chats in valence, arousal, and dominance space”,2023,“Sentiment analysis is a vastly established domain for social media monitoring, feedback insights, and commercial or political campaigns. It allows us to gain an overview of the wider public opinion on certain topics. Nowadays, different social media platforms play a crucial role in web-based sentiment analysis and emotion detection from distinct perspectives. Likewise, WhatsApp is probably the most popular messaging app, allowing users to send messages, images, audio, or videos. However, it is still highly under-explored for any type of linguistic synthesis and analysis. Like many other groups of people, students use WhatsApp for various purposes, even more since the last two years of the pandemic phase, for instance, class communication, study group communication, etc. In this paper, we present a novel approach to analyze the sentiments and emotions of students in valence, arousal, and dominance space by classifying the messages from their WhatsApp group chat. The emotional dimensions of valence, arousal and dominance (VAD) can derive a person’s interest (attraction), level of activation, and perceived level of control for a particular situation from textual communication. We propose a vanilla SVM model fused with a language classifier to calculate each message’s sentiment ratings. Finally, using the SVM classifier, we classify the sentiment ratings concerning the degree of the VAD scale. The data were analyzed using a qualitative content analysis method. The results of the study in the form of cumulative sentiment scale and sentiment clustering in VAD space reveal that the students’ WhatsApp groups were mostly used for sharing information, exchanging ideas, and discussing issues, with mostly neutral to positive sentiment viewpoints for the provided topics of discussions. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Austria, part of Springer Nature.”,Arousal; Dominance; Machine learning; Sentiment classification; Valence; WhatsApp analysis
above contains Authors,Title,Year,Abstract,Author Keywords in a csv format create a litrature review according to relevent keywords for text / sentiment analysis explain each keyword and then what the paperss say about it