TSA-CNN-AOA: Twitter sentiment analysis using CNN optimized via arithmetic optimization algorithm

Aslan, Serpil; Kiziloluk, Soner; Sert, Eser

doi:10.1007/s00521-023-08236-2

Cited by 25 publications

(7 citation statements)

References 58 publications

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“…The performance of the proposed SA-BDRNN Scheme is evaluated by conducting simulation experiments and analysis for different parameters including Size of Vector [30] and Vocabulary, number of hidden layers [31], Count and Size of filters, drop out, Regularizer and Activation functions as shown in table 5. Initially, the prevalence of the proposed SA-BDRNN Scheme is established for conducting sentimental analysis.…”

Section: Simulation Results and Discussionmentioning

confidence: 99%

BIDRN: A Method of Bidirectional Recurrent Neural Network for Sentiment Analysis

D Muthusankar

2023

IJRITCC

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Text mining research has grown in importance in recent years due to the tremendous increase in the volume of unstructured textual data. This has resulted in immense potential as well as obstacles in the sector, which may be efficiently addressed with adequate analytical and study methods. Deep Bidirectional Recurrent Neural Networks are used in this study to analyze sentiment. The method is categorized as sentiment polarity analysis because it may generate a dataset with sentiment labels. This dataset can be used to train and evaluate sentiment analysis models capable of extracting impartial opinions. This paper describes the Sentiment Analysis-Deep Bidirectional Recurrent Neural Networks (SA-BDRNN) Scheme, which seeks to overcome the challenges and maximize the potential of text mining in the context of Big Data. The current study proposes a SA-DBRNN Scheme that attempts to give a systematic framework for sentiment analysis in the context of student input on institution choice. The purpose of this study is to compare the effectiveness of the proposed SA- DBRNN Scheme to existing frameworks to establish a robust deep neural network that might serve as an adequate classification model in the field of sentiment analysis.

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Section: Simulation Results and Discussionmentioning

confidence: 99%

BIDRN: A Method of Bidirectional Recurrent Neural Network for Sentiment Analysis

D Muthusankar

2023

IJRITCC

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“…It measures to adopt the strategies to fight against coronavirus which has affected socially and economically around the world. The author presented in [41] TSA to examine the data on social media that uses CNN enriched via an arithmetic optimization algorithm approach to extract the features that are applied to obtain the data from CNN for the selection process. In order to create a useable database utilizing FastText Skip-gram and the CNN model as a feature extractor, this work has built an API with 173,638 tweets concerning COVID-19 that were collected from X between July, 2020, and August, 2020.…”

Section: Literature Surveymentioning

confidence: 99%

Hybrid Deep Learning Approach for Classification and Analysis of X Posts on Russia Ukraine War

Suman,

Varshney,

Chouhan

et al. 2024

IJCDS

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X (formerly Twitter) has become a vital source of information on various variety of social, political, and economic concerns, as a consequence of its growth and popularity which has resulted in an enormous number of people sharing their opinions on a wide range of areas. To determine people's emotions about the Russia-Ukraine war (RUW), this study examines trends in English-language tweets. In this work, we have engaged 34 countries to tweet opinions that produce a strong perception of the people about the war and message to the world what people famine from the countries and that affects their lives. To analyze positive and negative emotions in tweets, which are represented by hope and fear, the LSTM-CNN model is based on deep learning. A time series is calculated that correlates with the rate of recurrence of negative and positive tweets in different nations. Additionally, an approach based on the average of the neighborhood has been used for modelling and grouping the time series of various countries. The clustering method gives results as significant information, how people feel about this dispute and share their opinions about RUW is approached. When compare on different models on overall data that the 96% accuracy is Achieved by the LSTM-CNN model. 97.09% accuracy, is achieved when the comparing the tweets from the cluster 1 countries. When comparing the tweets from the cluster 2 countries the 99% accuracy, is achieved. 97% accuracy is achieved by comparing the tweets from cluster 3 countries. 97% accuracy, is achieved when the comparing the tweets from cluster 4 countries. 96% accuracy is achieved by the LSTM-CNN model when the comparing the tweets from cluster 5 countries by the different models. This research study helps the uninfluenced press members to have an impartial source of information for their reports and articles.

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“…In their study, Teaching-Learning-Based Optimization (TLBO) is integrated with Long Short-Term Memory (LSTM) networks to analyze sentiments on Twitter for predicting stock prices. Additionally, Saranya and Usha [16] and Aslan et al [17] have employed advanced machine learning techniques such as Random Forest and Convolutional Neural Networks (CNN) combined with Arithmetic Optimization Algorithm (AOA). These methodologies have been applied to multi-class sentiment classification, yielding high accuracies in their respective studies.…”

Section: Related Workmentioning

confidence: 99%

Sentiment Analysis in Social Networks during the 2021 and 2022 Formula 1 Seasons: A Study Using Natural Language Processing on Twitter

Mileski,

Prado Campos,

Fernando Carvalho

et al. 2024

Anais Do XV Computer on the Beach - COTB'24

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Sentiment Analysis is an emerging research area that focuses onextracting semantic and emotional inferences from natural language,paving the way for analyses that deal with a high volume oftextual data. The growing importance of data in strategic decisionmakingand the recognition of social networks as vast repositoriesof public opinion have propelled this study, which aimed to explorethe interaction between human emotions and motorsport events.Thus, this study focused on applying Natural Language Processingto extract and analyze sentiments expressed in tweets about Formula1. Advanced machine learning and deep learning techniqueswere employed to train various models in the sentiment classificationtask. Among these, Logistic Regression and LSTMs stood out,achieving accuracies of 78.21% and 78.08%, respectively. The LSTMmodel, in particular, was implemented on a public dataset of tweetscollected during the 2021 and 2022 Formula 1 seasons. The modelwas used to classify the sentiments expressed by fans, allowingfor an exploratory analysis of data correlated to specific events ofthe races. The findings revealed significant engagement patterns,with notable spikes in emotional reactions coinciding with criticalmoments of the seasons. These discoveries illustrate how particularevents can profoundly influence the emotions and behavior of fans.From a detailed analysis of expressed sentiments, valuable data canbe obtained that may be leveraged for developing more effectivemarketing and communication strategies in the sport.

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TSA-CNN-AOA: Twitter sentiment analysis using CNN optimized via arithmetic optimization algorithm

Cited by 25 publications

References 58 publications

BIDRN: A Method of Bidirectional Recurrent Neural Network for Sentiment Analysis

BIDRN: A Method of Bidirectional Recurrent Neural Network for Sentiment Analysis

Hybrid Deep Learning Approach for Classification and Analysis of X Posts on Russia Ukraine War

Sentiment Analysis in Social Networks during the 2021 and 2022 Formula 1 Seasons: A Study Using Natural Language Processing on Twitter

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