Fuzzy logic and Genetic Algorithm based Text Classification Twitter

Abdul-Jaleel, Mohammed; Ali, Yossra H.; Ibrahim, Nuha Jameel

doi:10.1109/sccs.2019.8852607

Cited by 8 publications

(6 citation statements)

References 7 publications

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“…Recognizing the limitations of unsupervised approaches in handling complex datasets, the authors provide a solution to this problem by proposing the utilization of K-NN rule and fuzzy logic for effective outlier detection. In [31], a hybrid method based on fuzzy logic and Genetic Algorithm applied to the text classification Twitter is proposed. In [32], the approach combines Fuzzy C-Means clustering and a fuzzy inference system for an audiovisual quality of experience.…”

Section: Fuzzy Setsmentioning

confidence: 99%

Modeling of Fuzzy Systems Based on the Competitive Neural Network

Barraza,

Melin,

Valdez

et al. 2023

Applied Sciences

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This paper presents a method to dynamically model Type-1 fuzzy inference systems using a Competitive Neural Network. The aim is to exploit the potential of Competitive Neural Networks and fuzzy logic systems to generate an intelligent hybrid model with the ability to group and classify any dataset. The approach uses the Competitive Neural Network to cluster the dataset and the fuzzy model to perform the classification. It is important to note that the fuzzy inference system is generated automatically from the classes and centroids obtained with the Competitive Neural Network, namely, all the parameters of the membership functions are adapted according to the values of the input data. In the approach, two fuzzy inference systems, Sugeno and Mamdani, are proposed. Additionally, variations of these models are presented using three types of membership functions, including Trapezoidal, Triangular, and Gaussian functions. The proposed models are applied to three classification datasets: Wine, Iris, and Wisconsin Breast Cancer (WDBC). The simulations and results present higher classification accuracy when implementing the Sugeno fuzzy inference system compared to the Mamdani system, and in both models (Mamdani and Sugeno), better results are obtained when the Gaussian membership function is used.

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Section: Fuzzy Setsmentioning

confidence: 99%

Modeling of Fuzzy Systems Based on the Competitive Neural Network

Barraza,

Melin,

Valdez

et al. 2023

Applied Sciences

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“…Vashishtha & Susan proposed an adaptive fuzzy neural network, the method uses multiple dictionaries to extract features from social media posts and perform sentiment analysis, The proposed model is called MultiLexANFIS [58], they also developed a novel unsupervised model based on fuzzy rules for the sentiment analysis problem and achieved good results on dictionaries in different domains [59]. Abdul-Jaleel et al developed a new model to address the sentiment analysis problem, the method combines the feature set extracted from emotional sentences by genetic algorithm and fuzzy logic theory to improve the feature selection of emotional sentences, the results show that it is better than ordinary keyword feature selection [60]. AL-Deen et al proposed a sentiment classification method based on the fuzzy rule system, the research combined the fuzzy rule system that called FBPS, to extract features and used the crow algorithm (CSA) to strengthen the fuzzy output., the results found that compared with the existing machine learning methods, the results obtained higher accuracy [61], Dragoni & Pertucci proposed a method for multi-domain sentiment analysis by finding common word overlap through fuzzy methods [62], Crockett et al evaluated the Fuzzy Semantic Similarity Measure (FSSM) to analyze posts on social media to predict major events that may occur in the future, the results show that relevant keywords will ferment and spread on social media before potential events occur [63], Sassi et al believed that posts on social networks usually have several different emotions, so they proposed an automatic method based on semantic similarity measurement, using fuzzy classification to identify article emotions from online social software [64].…”

Section: A Text Classificationmentioning

confidence: 99%

Review of Survey Research in Fuzzy Approach for Text Mining

Lai

Chen

2023

IEEE Access

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Text mining has been common research field, with the emergence of Web 2.0 and the development of social software, the amount of text generated every day has increased dramatically. The texts contain a lot of valuable information, how to analysis for the information from text is very important. Therefore, many research have explored related methods and various fields of text mining, such as sentiment analysis, text clustering, text summarization, etc., However, unlike other numerical data that can be directly calculated in terms of character performance, the calculation must be performed after vector conversion, and the words may have polysemy, which provides more challenges for natural language processing. Due to the above challenges, various techniques are used for data preprocessing before analysis. In addition to the common statistical and discrete methods for text data, methods based on fuzzy logic provide another option for effective natural language analysis, Therefore, in recent years, more and more studies have added Fuzzy logic to additionally capture the context semantics of individual words to help more accurate natural language processing. This survey research discusses multiple text mining methods, The survey paper discusses multiple text mining methods, subfields and application fields, covering the literature published between 2010 and 2022, It is organized to the subtasks to be performed, the methods and natural language processing techniques used, and the application scenarios. At the end of this study, the research has provided the key point discussion and relevant suggestions for text mining combined with fuzzy logic.

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“…Abdul-Jaleel et al [43] introduced a new model to solve the SA issue. This model combines a genetic algorithm with fuzzy logic theory.…”

Section: Literature Reviewmentioning

confidence: 99%

Sentence-Level Classification Using Parallel Fuzzy Deep Learning Classifier

et al. 2021

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At present, with the growing number of Web 2.0 platforms such as Instagram, Facebook, and Twitter, users honestly communicate their opinions and ideas about events, services, and products. Owing to this rise in the number of social platforms and their extensive use by people, enormous amounts of data are produced hourly. However, sentiment analysis or opinion mining is considered as a useful tool that aims to extract the emotion and attitude from the user-posted data on social media platforms by using different computational methods to linguistic terms and various Natural Language Processing (NLP). Therefore, enhancing text sentiment classification accuracy has become feasible, and an interesting research area for many community researchers. In this study, a new Fuzzy Deep Learning Classifier (FDLC) is suggested for improving the performance of data-sentiment classification. Our proposed FDLC integrates Convolutional Neural Network (CNN) to build an effective automatic process for extracting the features from collected unstructured data and Feedforward Neural Network (FFNN) to compute both positive and negative sentimental scores. Then, we used the Mamdani Fuzzy System (MFS) as a fuzzy classifier to classify the outcomes of the two used deep (CNN+FFNN) learning models in three classes, which are: Neutral, Negative, and Positive. Also, to prevent the long execution time taking by our hybrid proposed FDLC, we have implemented our proposal under the Hadoop cluster. An experimental comparative study between our FDLC and some other suggestions from the literature is performed to demonstrate our offered classifier's effectiveness. The empirical result proved that our FDLC performs better than other classifiers in terms of true positive rate, true negative rate, false positive rate, false negative rate, error rate, precision, classification rate, kappa statistic, F1-score and time consumption, complexity, convergence, and stability.

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Fuzzy logic and Genetic Algorithm based Text Classification Twitter

Cited by 8 publications

References 7 publications

Modeling of Fuzzy Systems Based on the Competitive Neural Network

Modeling of Fuzzy Systems Based on the Competitive Neural Network

Review of Survey Research in Fuzzy Approach for Text Mining

Sentence-Level Classification Using Parallel Fuzzy Deep Learning Classifier

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