Sentiment Analysis on Twitter Data of World Cup Soccer Tournament Using Machine Learning

Patel, Ravikumar; Passi, Kalpdrum

doi:10.3390/iot1020014

Cited by 52 publications

(42 citation statements)

References 11 publications

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“…The third experiment assesses the Chi-square, Information Gain, Gain Ratio, and Gini Index approaches in order to determine the approach with high classification rate. The fourth experiment is did to prove the effectiveness of our suggested approach in terms of AC, TPR, ER, TNR, FS, FPR, KS, FNR, PR, and TC compared to ID3, C4.5, Soni et al [39], Ngoc et al [40], Lakshmi et al [51], AitAddi et al [56], Patel et al [57], and Wang et al [58] approaches. Finally, the fifth experiment is performed to proved the performance of our classifier in terms of complexity, stability and convergence compared to Soni et al [39], Ngoc et al [40], Lakshmi et al [51], AitAddi et al [56], Patel et al [57], and Wang et al [58] classifiers.…”

Section: B Results and Discussionmentioning

confidence: 99%

“…The newest innovative research papers for the OM are Lakshmi et al [51]; Guerreiro et al [52]; Mehta et al [53]; Zhang [54]; Lopez-Chau et al [55]; AitAddi et al [56]; Patel et al [57]; Wang et al [58] as presented in the Table 5. In [51], the authors proposed a new contribution that intends to classify reviews using two classifiers and determine which of the both perform better performance.…”

Section: Previous Researchmentioning

confidence: 99%

“…empirical outcomes show that their proposed method obtains considerable improvements over other literature approaches. In Patel et al [57], The authors performed a sentiment analysis on Twitter data for World Cup soccer 2014 held in Brazil to extract the emotions of the people everywhere in the world, utilizing machine learning algorithms. After the application of NLP operations like part-of-speech, word tokenization, lemmatization, etc., the SVM, NB, KNN have been applied as the classifiers to extract the emotions from the tweet.…”

Section: Previous Researchmentioning

confidence: 99%

See 2 more Smart Citations

A MapReduce Opinion Mining for COVID-19-Related Tweets Classification Using Enhanced ID3 Decision Tree Classifier

Es-sabery

Qadir

et al. 2021

IEEE Access

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Opinion Mining (OM) is a field of Natural Language Processing (NLP) that aims to capture human sentiment in the given text. With the ever-spreading of online purchasing websites, micro-blogging sites, and social media platforms, OM in online social media platforms has picked the interest of thousands of scientific researchers. Because the reviews, tweets and blogs acquired from these social media networks, act as a significant source for enhancing the decision making process. The obtained textual data (reviews, tweets, or blogs) are classified into three different class labels which are negative, neutral and positive for analyzing and extracting relevant information from the given dataset. In this contribution, we introduce an innovative MapReduce improved weighted ID3 decision tree classification approach for OM, which consists mainly of three aspects: Firstly We have used several feature extractors to efficiently detect and capture the relevant data from the given tweets, including N-grams or character-level, Bag-Of-Words, word embedding (GloVe, Word2Vec), FastText, and TF-IDF. Secondly, we have applied a multiple feature selector to reduce the high feature's dimensionality, including Chi-square, Gain Ratio, Information Gain, and Gini Index. Finally, we have employed the obtained features to carry out the classification task using an improved ID3 decision tree classifier, which aims to calculate the weighted information gain instead of information gain used in traditional ID3. In other words, to measure the weighted information gain for the current conditioned feature, we follow two steps: First, we compute the weighted correlation function of the current conditioned feature. Second, we multiply the obtained weighted correlation function by the information gain of this current conditioned feature. This work is implemented in a distributed environment using the Hadoop framework, with its programming framework MapReduce and its distributed file system HDFS. Its primary goal is to enhance the performance of a well-known ID3 classifier in terms of accuracy, execution time, and ability to handle the massive datasets. We have carried out several experiences that aims to assess the effectiveness of our suggested classifier compared to some other contributions chosen from the literature. The experimental results demonstrated that our ID3 classifier works better on COVID-19_Sentiments dataset than other classifiers in terms of Recall (85.72 %), specificity (86.51 %), error rate (11.18 %), false-positive rate (13.49 %), execution time (15.95s), kappa statistic (87.69 %), F1-score (85.54 %), classification rate (88.82 %), false-negative rate (14.28 %), precision rate (86.67 %), convergence (it convergent towards the iteration 90), stability (it is more stable with mean deviation standard equal to 0.12 %), and complexity (it requires much lower time and space computational complexity).

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Section: B Results and Discussionmentioning

confidence: 99%

Section: Previous Researchmentioning

confidence: 99%

Section: Previous Researchmentioning

confidence: 99%

See 1 more Smart Citation

A MapReduce Opinion Mining for COVID-19-Related Tweets Classification Using Enhanced ID3 Decision Tree Classifier

Es-sabery

Qadir

et al. 2021

IEEE Access

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“…In the negative class classification, the three methods have the same F-1, but in the positive class classification, the Linear SVM shows the best performance with an F-1 Score value of 0.66 compared to the other two methods. (Redhu, Srivastava, Bansal, & Gupta, 2018) Patel & Passi (Patel & Passi, 2020) compared the Naïve Bayes, Support Vector Machine, Random Forest, and K-Nearest Neighbor methods to classify reviews about the 2014 World Cup into three class: positive neutral, and negative. Data taken from Twitter in June-July 2014 and obtained about 2 million tweets.…”

Section: Literature Reviewmentioning

confidence: 99%

Sentiment analysis on myindihome user reviews using support vector machine and naïve bayes classifier method

Hakim

Putra

Khasanah

2021

Int. J. Ind. Optim.

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In the era of globalization, the internet has become a human need in doing various things. Many internet users are an opportunity for internet service providers, PT Telekomunikasi Indonesia (Telkom). One of PT Telkom's products is IndiHome. As the only state-owned enterprise engaged in telecommunications, PT Telkom is expected to meet the needs of the Indonesian people. However, based on the rating obtained by IndiHome products through the myIndiHome application on Google Play, it is 3.5 out of 87,000 more reviews. The reviews focus on how important the effect of word-of-mouth is on choosing and using internet provider products. The review data was collected on November 1, 2020 to December 15, 2020, with a total of 2,539 reviews as a sample. The sentiment analysis process that has been carried out shows that the number of reviews included in the negative sentiment class was 1.160 reviews, and the positive class was 1.374 reviews out of a total of 2,539 reviews. The results indicate that service errors in IndiHome services are still quite high, reaching 46.7% as indicated by the number of negative reviews. The classification results show that the average value of the total accuracy of the Support Vector Machine (SVM) method is 86.54% greater than Naïve Bayes Classifier (NBC) method which has an average total accuracy of 84.69%. Based on fishbone diagram analysis, there are 12nd problems on negative reviews that classify problems 5P factors: Price, People, Process, Place, and Product.

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“…This presents a problem as the neural network sentiment analysis is commonly domain-specific. Another work [ 43 ] also examined sentiment analysis on Twitter data of the World Cup soccer 2014, but explore the overall sentiment of the event and not concussion specific.…”

Section: Related Workmentioning

confidence: 99%

A deep neural network approach for sentiment analysis of medically related texts: an analysis of tweets related to concussions in sports

et al. 2021

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Annually, over three million people in North America suffer concussions. Every age group is susceptible to concussion, but youth involved in sporting activities are particularly vulnerable, with about 6% of all youth suffering a concussion annually. Youth who suffer concussion have also been shown to have higher rates of suicidal ideation, substance and alcohol use, and violent behaviors. A significant body of research over the last decade has led to changes in policies and laws intended to reduce the incidence and burden of concussions. However, it is also clear that youth engaging in high-risk activities like sport often underreport concussion, while others may embellish reports for specific purposes. For such policies and laws to work, they must operate effectively within a facilitative social context so understanding the culture around concussion becomes essential to reducing concussion and its consequences. We present an automated deep neural network approach to analyze tweets with sport-related concussion context to identify the general public’s sentiment towards concerns in sport-related concussion. A single-layer and multi-layer convolutional neural networks, Long Short-Term Memory (LSTM) networks, and Bidirectional LSTM were trained to classify the sentiments of the tweets. Afterwards, we train an ensemble model to aggregate the predictions of our networks to provide a final decision of the tweet’s sentiment. The system achieves an evaluation F1 score of 62.71% based on Precision and Recall. The trained system is then used to analyze the tweets in the FIFA World Cup 2018 to measure audience reaction to events involving concussion. The neural network system provides an understanding of the culture around concussion through sentiment analysis.

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Sentiment Analysis on Twitter Data of World Cup Soccer Tournament Using Machine Learning

Cited by 52 publications

References 11 publications

A MapReduce Opinion Mining for COVID-19-Related Tweets Classification Using Enhanced ID3 Decision Tree Classifier

A MapReduce Opinion Mining for COVID-19-Related Tweets Classification Using Enhanced ID3 Decision Tree Classifier

Sentiment analysis on myindihome user reviews using support vector machine and naïve bayes classifier method

A deep neural network approach for sentiment analysis of medically related texts: an analysis of tweets related to concussions in sports

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