Aspect based Fuzzy Logic Sentiment Analysis on Social Media Big Data

Maheswari, S. Uma; Dhenakaran, S. S.

doi:10.1109/iccsp48568.2020.9182174

Cited by 8 publications

(10 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Its performance is better than Xu et al [21], because it Mamdani fuzzy system as the classifier, which is very efficient in dealing with inherent and ambiguous data. Although this approach [22] has lower performance compared to the remainder evaluated approaches because the AFINN word dictionary has a limited capacity for detecting all relevant features. Liao et al [9] has achieved an accuracy equals 75 .32 %, and 59 % in the case COVID-19 Sentiments and Sentiment140 datasets, respectively, which is a good performance compared to both [21], and [22] classifiers.…”

Section: Experiments and Resultsmentioning

confidence: 94%

“…3, Xu et al [21] has achieved a lower accuracy (51.44 %, and 33.09 % in the case COVID-19 Sentiments and Sentiment140 datasets, respectively) compared to other classifiers because the authors of this approach [21] do not give great importance for text preprocessing tasks. Maheswari et al [22] has achieved an accuracy equals 63.84 %, and 45.97 % in the case COVID-19 Sentiments and Sentiment140 datasets, respectively. Its performance is better than Xu et al [21], because it Mamdani fuzzy system as the classifier, which is very efficient in dealing with inherent and ambiguous data.…”

Section: Experiments and Resultsmentioning

confidence: 96%

“…This section introduces in depth the performance of the suggested hybrid system that combines several preprocessing techniques, FastText word embedding method [20], convolutional neural network, gaussian membership function, fuzziness C4.5 decision tree, general fuzziness reasoning method and Hadoop ecosystem. The performance of our suggested hybrid system and other hybrid approaches selected from the literature (Xu et al [21], Liao et al [9], Maheswari et al [22], and Es-sabery et al [14]) is assessed by measuring five assessment criteria as discussed in below: From Fig. 2, we deduced that our proposed hybrid model achieved (92.31%, 94.56%) in precision, (90.58%, 95.74%) in recall, and (92.06%, 94.63%) in fmeasure on the Sentiment140 and COVID-19 Sentiments datasets respectively.…”

Section: Experiments and Resultsmentioning

confidence: 99%

“…Fig. 3, illustrates the empirical results attained in terms of accuracy implementing our proposed hybrid model, Xu et al [21], Liao et al [9], Maheswari et al [22], and Es-sabery et al [14] classifiers over Sentiment140 and COVID-19 Sentiments datasets in order to prove the efficiency of our suggested hybrid classifier by comparing its achieved performance to the experimental performance attained with selected classifiers. As depicted in Fig.…”

Section: Experiments and Resultsmentioning

confidence: 99%

See 3 more Smart Citations

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

Es-sabery

Garmani

et al. 2021

E3S Web Conf.

View full text Add to dashboard Cite

This contribution proposes a new model for sentiment analysis, which combines the convolutional neural network (CNN), C4.5 decision tree algorithm, and Fuzzy Rule-Based System (FRBS). Our suggested method consists of six parts. Firstly we have applied several pre-processing techniques. Secondly, we have used the fastText method for vectoring the analysed tweets. Thirdly, we have implemented the CNN for extracting and selecting the pertinent features from the tweets. Fourthly, we have fuzzified the CNN output using the Gaussian Fuzzification (GF) method for coping with vague data. Then we have applied fuzziness C4.5 for creating the fuzziness rules. Finally, we have used the General Fuzziness Reasoning (GFR) approach for classifying the new tweets. In summary, our method integrates the advantages of CNN and C4.5 techniques and overcomes the shortcomings of ambiguous data in the tweets using FRBS, which is consists of three-phase: fuzzification phase using GF, inference mechanism using fuzziness C4.5, and defuzzification phase using GFR. Also, to give our approach the ability to deal with the massive data, we have implemented it on the Hadoop framework of five computers. The experiential findings confirmed that our model operates excellently compared to other chosen models form the literature.

show abstract

Section: Experiments and Resultsmentioning

confidence: 94%

Section: Experiments and Resultsmentioning

confidence: 96%

Section: Experiments and Resultsmentioning

confidence: 99%

Section: Experiments and Resultsmentioning

confidence: 99%

See 2 more Smart Citations

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

Es-sabery

Garmani

et al. 2021

E3S Web Conf.

View full text Add to dashboard Cite

show abstract

“…The frequency of the aspect was estimated in the dataset. Meanwhile, Maheswari and Dhenakaran [20] used a dictionary for opinion words and Fuzzy rules.…”

Section: Related Workmentioning

confidence: 99%

A new approach for extracting and scoring aspect using SentiWordNet

Tran

Duangsuwan

Wettayaprasit

2021

IJEECS

View full text Add to dashboard Cite

Aspect-based online information on social media plays a vital role in influencing people’s opinions when consumers concern with their decisions to make a purchase, or companies intend to pursue opinions on their product or services. Determining aspect-based opinions from the online information is necessary for business intelligence to support users in reaching their objectives. In this study, we propose the new aspect extraction and scoring system which has three procedures. The first procedure is normalizing and tagging part-of-speech for sentences of datasets. The second procedure is extracting aspects with pattern rules. The third procedure is assigning scores for aspects with SentiWordNet. In the experiments, benchmark datasets of customer reviews are used for evaluation. The performance evaluation of our proposed system shows that our proposed system has high accuracy when compared to other systems.

show abstract