Sentiment Analysis of Covid19 Tweets Using A MapReduce Fuzzified Hybrid Classifier Based On C4.5 Decision Tree and Convolutional Neural Network

Es-sabery, Fatima; Es-sabery, Khadija; Garmani, Hamid; Haïr, Abdellatif

doi:10.1051/e3sconf/202129701052

Cited by 5 publications

(4 citation statements)

References 22 publications

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“…C4.5 is an algorithm improved from ID3, which uses attribute selection measures to select attributes with the largest Information Gain. C4.5 is performed by several domains, such as customer purchase behavior prediction [31], landslide susceptibility analyses [32], education [33], and sentiment analysis [34]. The second algorithm is support vector machine (SVM) [35].…”

Section: Customer Need Phrases Classificationmentioning

confidence: 99%

Customer Needs Classification from Online Social Media Using Bag-of-Concepts Representation

Laosen,

Intana,

Chuaynukul

2023

Int. J. Adv. Sci. Eng. Inf. Technol.

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Social media platforms are now a very powerful tool for digital marketing strategy because it helps companies to be in direct contact with their customers. A communication problem in digital social media is several customer needs phrases posted on social media, making it difficult for businesses to find relevant posts and respond to customers immediately. Therefore, knowing and understanding the customer requirements for a product can help the product owner to propose the right product to the right customer. This study focuses on understanding customer needs in Thai and classifying them into certain concepts. This study aims to classify customer needs for products in online social media community groups. The model focuses on understanding Thai customer need phrases. We then use a bag of concepts representation, including pattern analysis that applies n-grams together with POS and synonym replacement, conceptual analysis, pattern matching, and class labeling that applies concept sets obtained from the FP-Growth algorithm and represents TD-IDF value in a bag of concepts. The effectiveness of the proposed model is evaluated on five classification algorithms, including Decision Tree, Support Vector Machine, NaÃ¯ve-Bayes, K-Nearest Neighbor, and RBF Neural Network. The results show that Decision Tree can yield higher accuracy and F-measure values than the others. As this study is an initial step of a personalized product recommendation system in the future, this study will apply the model to the remaining domains for future work.

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Section: Customer Need Phrases Classificationmentioning

confidence: 99%

Customer Needs Classification from Online Social Media Using Bag-of-Concepts Representation

Laosen,

Intana,

Chuaynukul

2023

Int. J. Adv. Sci. Eng. Inf. Technol.

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“…TF_IDF means term frequency -inverse document frequency, which is widely recognized and it is utilized as a weighting procedure and its performance is also still very comparable with new approaches [27]. It is a statistical value that is meant to reflect how much weight a given word has to a certain document in a corpus or a collection.…”

Section: B Tf_idf Extractormentioning

confidence: 99%

“…-It is efficient in handling textual mistakes and character identification issues that is because of the N-gram structure [27].…”

Section: Advantages and Disadvantages Of Each Extractormentioning

confidence: 99%

Evaluation of different extractors of features at the level of sentiment analysis

Es-sabery¹,

Es-sabery²,

Garmani³

et al. 2022

Infocommunications journal

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Sentiment analysis is the process of recognizing and categorizing the emotions being expressed in a textual source. Tweets are commonly used to generate a large amount of sentiment data after they are analyzed. These feelings data help to learn about people's thoughts on a various range of topics. People are typically attracted for researching positive and negative reviews, which contain dislikes and likes, shared by the consumers concerning the features of a certain service or product. Therefore, the aspects or features of the product/ service play an important role in opinion mining. Furthermore to enough work being carried out in text mining, feature extraction in opinion mining is presently becoming a hot research field. In this paper, we focus on the study of feature extractors because of their importance in classification performance. The feature extraction is the most critical aspect of opinion classification since classification efficiency can be degraded if features are not properly chosen. A few scientific researchers have addressed the issue of feature extraction. And we found in the literature that almost every article deals with one or two feature extractors. For that, we decided in this paper to cover all the most popular feature extractors which are BOW, N-grams, TF-IDF, Word2vec, GloVe and FastText. In general, this paper will discuss the existing feature extractors in the opinion mining domain. Also, it will present the advantages and the inconveniences of each extractor. Moreover, a comparative study is performed for determining the most efficient combination CNN/extractor in terms of accuracy, precision, recall, and F1 measure.

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“…As a consequence, the worldwide brands have recognised Twitter as a fully integrated advertising and marketing platform and are utilizing it in innovative ways to feed their promotional campaigns [7]. Twitter is equally being used as a political campaign platform by making it an incorporated media at the core of the policy communications strategies [8].…”

Section: Introductionmentioning

confidence: 99%

Emotion Processing by Applying a Fuzzy-Based Vader Lexicon and a Parallel Deep Belief Network Over Massive Data

et al. 2022

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Emotion processing has been a very intense domain of investigation in data analysis and NLP during the previous few years. Currently, the algorithms of the deep neural networks have been applied for opinion mining tasks with good results. Among various neuronal models applied for opinion mining a deep belief network (DBN) model has gained more attention. In this proposal, we have developed a combined classifier based on fuzzy Vader lexicon and a parallel deep belief network for emotion analysis. We have implemented multiple pretreatment techniques to improve the quality and soundness of the data and eliminate disturbing data. Afterward, we have performed a semi-automatic dataset labeling using a combination of two different methods: Mamdani's fuzzy system and Vader lexicon. As well, we have applied four feature extractors, which are: GloVe, TFIDF (Trigram), TFIDF (Bigram), TFIDF (Unigram) with the aim of transforming each incoming tweet into a digital value vector. In addition, we have integrated three feature selectors, namely: The ANOVA method, the chi-square approach and the mutual information technique with the objective of selecting the most relevant features. Further, we have implemented the DBN as classifier for classifying each inputted tweet into three categories: neutral, positive or negative. At the end, we have deployed our proposed approach in parallel way employing both Hadoop and Spark framework with the purpose of overcoming the problem of long runtime of massive data. Furthermore, we have carried out a comparison between our newly suggested hybrid approach and alternative hybrid models available in the literature. From the experimental findings, it was found that our suggested vague parallel approach is more powerful than the baseline patterns in terms of false negative rate (1.33%), recall (99.75%), runtime (32.95s), convergence, stability, F1 score (99.53%), accuracy (98.96%), error rate (1.04%), kappa-Static (99.1%), complexity, false positive rate (0.25%), precision rate (97.59%) and specificity rate (98.67%). As a conclusion, our vague parallel approach outperforms baseline and deep learning models, as well as certain other approaches chosen from the literature.

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Sentiment Analysis of Covid19 Tweets Using A MapReduce Fuzzified Hybrid Classifier Based On C4.5 Decision Tree and Convolutional Neural Network

Cited by 5 publications

References 22 publications

Customer Needs Classification from Online Social Media Using Bag-of-Concepts Representation

Customer Needs Classification from Online Social Media Using Bag-of-Concepts Representation

Evaluation of different extractors of features at the level of sentiment analysis

Emotion Processing by Applying a Fuzzy-Based Vader Lexicon and a Parallel Deep Belief Network Over Massive Data

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