Sentiment classification within online social media using whale optimization algorithm and social impact theory based optimization

Akyol, Sinem; Alataş, Bilal

doi:10.1016/j.physa.2019.123094

Cited by 32 publications

(17 citation statements)

References 52 publications

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“…Akyol et al [30] describes a Social Impact Theory depend Optimization Algorithm and whale optimization model based on opinion mining. The prevalent exchanging methodology dependent on the sentiment feedback quality between the tweets and news utilizing conventional programming optimization strategy was discussed by Yang et al [31].…”

Section: Literature Reviewmentioning

confidence: 99%

An efficient sentiment analysis methodology based on long short-term memory networks

Shobana

Murali

2021

Complex Intell. Syst.

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Sentiment analysis is the process of determining the sentiment polarity (positivity, neutrality or negativity) of the text. As online markets have become more popular over the past decades, online retailers and merchants are asking their buyers to share their opinions about the products they have purchased. As a result, millions of reviews are generated daily, making it difficult to make a good decision about whether a consumer should buy a product. Analyzing these enormous concepts is difficult and time-consuming for product manufacturers. Deep learning is the current research interest in Natural language processing. In the proposed model, Skip-gram architecture is used for better feature extraction of semantic and contextual information of words. LSTM (long short-term memory) is used in the proposed model for understanding complex patterns in textual data. To improve the performance of the LSTM, weight parameters are optimized by the adaptive particle Swarm Optimization algorithm. Extensive experiments were conducted on four datasets proved that our proposed APSO-LSTM model secured higher accuracy over the classical methods such as traditional LSTM, ANN, and SVM. According to simulation results, the proposed model is outperforming other existing models in different metrics.

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Section: Literature Reviewmentioning

confidence: 99%

An efficient sentiment analysis methodology based on long short-term memory networks

Shobana

Murali

2021

Complex Intell. Syst.

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“…The algorithm employed in different works and proves its superiority, especially in SA. Few works have been introduced, for example, the authors of [27] proposed an SA approach using WOA for feature selection mechanism, while the work in [28] provided a sentiment classification method in the social media environment by utilizing WOA.…”

Section: Introductionmentioning

confidence: 99%

An Evolutionary-Based Sentiment Analysis Approach for Enhancing Government Decisions during COVID-19 Pandemic: The Case of Jordan

et al. 2021

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The world has witnessed recently a global outbreak of coronavirus disease (COVID-19). This pandemic has affected many countries and has resulted in worldwide health concerns, thus governments are attempting to reduce its spread and impact on different aspects of life such as health, economics, education, and politics by making emergent decisions and policies (e.g., lockdown and social distancing). These new regulations influenced people’s daily life and cast significant burdens, concerns, and disparities on various population groups. Taking the wrong actions and enforcing bad decisions by some countries result in increasing the contagion rate and more catastrophic results. People start to post their opinions and feelings about their government’s decisions on different social media networks, and the data received through these platforms present a very useful source of information that affects how governments perceive and cope with the current the pandemic. Jordan was one of the top affected countries. In this paper, we proposed a decision support system based on the sentiment analysis mechanism by combining support vector machines with a whale optimization algorithm for automatically tuning the hyperparameters and performing feature weighting. The work is based on a hybrid evolutionary approach that aims to perform sentiment analysis combined with a decision support system to study people’s posts on Facebook to investigate their attitudes and feelings toward the government’s decisions during the pandemic. The government regulations were divided into two periods: the first and latter regulations. Studying public sentiments during these periods allows decision-makers in the government to sense people’s feelings, alert them in case of possible threats, and help in making proactive actions if needed to better handle the current pandemic situation. Five different versions were generated for each of the two collected datasets. The results demonstrate the superiority of the proposed Whale Optimization Algorithm & Support Vector Machines (WOA-SVM) against other metaheuristic algorithms and standard classification models as WOA-SVM has achieved 78.78% in terms of accuracy and 84.64% in term of f-measure, while other standard classification models such as NB, k-NN, J84, and SVM achieved an accuracy of 69.25%, 69.78%, 70.17%, and 69.29%, respectively, with 64.15%, 62.90%, 60.51%, and 59.09% F-measure. Moreover, when comparing our proposed WOA-SVM approach with other metaheuristic algorithms, which are GA-SVM, PSO-SVM, and MVO-SVM, WOA-SVM proved to outperform the other approaches with results of 78.78% in terms of accuracy and 84.64% in terms of F-measure. Further, we investigate and analyze the most relevant features and their effect to improve the decision support system of government decisions.

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“…Li et al [104] used WOA to modify the input weight and hidden layer bias of extreme learning machine (ELM) and used this model to assess the aging of insulated gate bipolar transistor module. Akyol and Alatas [105] adopted WOA for emotional analysis, which is a multiobjective problem. Qiao [106] introduced adaptive search and encircling mechanism, spiral position, and jump behavior to enhance the efficiency of WOA and used the improved algorithm to predict short-term gas consumption.…”

Section: Introductionmentioning

confidence: 99%

Spiral Motion Enhanced Elite Whale Optimizer for Global Tasks

et al. 2021

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The whale optimization algorithm (WOA) is a high-performance metaheuristic algorithm that can effectively solve many practical problems and broad application prospects. However, the original algorithm has a significant improvement in space in solving speed and precision. It is easy to fall into local optimization when facing complex or high-dimensional problems. To solve these shortcomings, an elite strategy and spiral motion from moth flame optimization are utilized to enhance the original algorithm’s efficiency, called MEWOA. Using these two methods to build a more superior population, MEWOA further balances the exploration and exploitation phases and makes it easier for the algorithm to get rid of the local optimum. To show the proposed method’s performance, MEWOA is contrasted to other superior algorithms on a series of comprehensive benchmark functions and applied to practical engineering problems. The experimental data reveal that the MEWOA is better than the contrast algorithms in convergence speed and solution quality. Hence, it can be concluded that MEWOA has great potential in global optimization.

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Sentiment classification within online social media using whale optimization algorithm and social impact theory based optimization

Cited by 32 publications

References 52 publications

An efficient sentiment analysis methodology based on long short-term memory networks

An efficient sentiment analysis methodology based on long short-term memory networks

An Evolutionary-Based Sentiment Analysis Approach for Enhancing Government Decisions during COVID-19 Pandemic: The Case of Jordan

Spiral Motion Enhanced Elite Whale Optimizer for Global Tasks

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