Application of location-based sentiment analysis using Twitter for identifying trends towards Indian general elections 2014

Almatrafi, Omaima; Parack, Suhem; Chavan, Bravim

doi:10.1145/2701126.2701129

Cited by 61 publications

(20 citation statements)

References 5 publications

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“…It is rather more complex since the data gathered is novel, and still in a raw unstructured state, without any prior insights and practical decision-making results. In the past, several works of such nature have been carried out, for instance, the 2012 U.S. presidential election [81], identifying the sentiment analysis trend for the 2014 Indian general election [82], political discourse analysis from Twitter for the 2016 US presidential elections [83], and analyzing the Twitter sentiments on the introduction of GST in India [84]. While the first three experimentations did not proceed for a sentiment classification or predictive accuracy approach, rather the researchers chose to represent an application-based approach for Twitter sentiment visualization on the discussed topics.…”

Section: Comparative Evaluationmentioning

confidence: 99%

Predicting the pandemic: sentiment evaluation and predictive analysis from large-scale tweets on Covid-19 by deep convolutional neural network

Das

Kolya²

2021

Evol. Intel.

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Engaging deep neural networks for textual sentiment analysis is an extensively practiced domain of research. Textual sentiment classification harnesses the full computational potential of deep learning models. Typically, these research works are carried either with a popular open-source data corpus, or self-extracted short phrase texts from Twitter, Reddit, or web-scrapped text data from other resources. Rarely do we see a large amount of data on a current ongoing event is being collected and cultured further. Also, an even more complex task would be to model the data from a currently ongoing event, not only for scaling the sentiment accuracy but also for making a predictive analysis for the same. In this paper, we propose a novel approach for achieving sentiment evaluation accuracy by using a deep neural network on live-streamed tweets on Coronavirus and future case growth prediction. We develop a large tweet corpus exclusively based on the Coronavirus tweets. We split the data into train and test sets, alongside we perform polarity classification and trend analysis. The refined outcome from the trend analysis helps to train the data to provide an incremental learning curvature for our neural network, and we obtain an accuracy of 90.67%. Finally, we provide a statistical-based future prediction for Coronavirus cases growth. Not only our model outperforms several previous state-of-art experiments in overall sentiment accuracy comparison for similar tasks, but it also maintains a throughout performance stability among all the test cases when tested with several popular open-source text corpora.

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Section: Comparative Evaluationmentioning

confidence: 99%

Predicting the pandemic: sentiment evaluation and predictive analysis from large-scale tweets on Covid-19 by deep convolutional neural network

Das

Kolya²

2021

Evol. Intel.

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“…Where the term ReLU refers to a non-linear activation method named a rectified linear unit as illustrated in (2); a in R indicates the applied bias and x represents the size of the employed filter S. Thus, the feature map M0 = [F0; F1; …; Fi+x-1] is constructed by the implementation of (1) in all selected window EW from the matrix E. Several filters Si:1z are exercised to create a set of feature maps MFi:1z.…”

Section: Features Extraction and Selectionmentioning

confidence: 99%

“…The Twitter platform comprises worthy data toward a variety of areas, such as economic, commercial, social, governmental, and political applications [2]. The analysis manually of Twitter's massive volume of data for extracting valuable information is very challenging.…”

Section: Introductionmentioning

confidence: 99%

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.

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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.

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“…Almatrafi et al [30] performed a location-based sentiment analysis through Indian general elections to determine the characteristics of tweets by utilizing machine learning techniques and natural language processing. Naive Bayes algorithm was utilized for the classification.…”

Section: Literature Reviewmentioning

confidence: 99%

Comparison of different machine learning techniques on location extraction by utilizing geo-tagged tweets: A case study

Eligüzel

Çetinkaya

Dereli

2020

Advanced Engineering Informatics

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In emergencies, Twitter is an important platform to get situational awareness simultaneously. Therefore, information about Twitter users' location is a fundamental aspect to understand the disaster effects. But location extraction is a challenging task. Most of the Twitter users do not share their locations in their tweets. In that respect, there are different methods proposed for location extraction which cover different fields such as statistics, machine learning, etc. This study is a sample study that utilizes geo-tagged tweets to demonstrate the importance of the location in disaster management by taking three cases into consideration. In our study, tweets are obtained by utilizing the "earthquake" keyword to determine the location of Twitter users. Tweets are evaluated by utilizing the Latent Dirichlet Allocation (LDA) topic model and sentiment analysis through machine learning classification algorithms including the Multinomial and Gaussian Naïve Bayes, Support Vector Machine (SVM), Decision Tree, Random Forest, Extra Trees, Neural Network, k Nearest Neighbor (kNN), Stochastic Gradient Descent (SGD), and Adaptive Boosting (AdaBoost) classifications. Therefore, 10 different machine learning algorithms are applied in our study by utilizing sentiment analysis based on location-specific disasterrelated tweets by aiming fast and correct response in a disaster situation. In addition, the effectiveness of each algorithm is evaluated in order to gather the right machine learning algorithm. Moreover, topic extraction via LDA is provided to comprehend the situation after a disaster. The gathered results from the application of three cases indicate that Multinomial Naïve Bayes and Extra Trees machine learning algorithms give the best results with an F-measure value over 80%. The study aims to provide a quick response to earthquakes by applying the aforementioned techniques.

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Application of location-based sentiment analysis using Twitter for identifying trends towards Indian general elections 2014

Cited by 61 publications

References 5 publications

Predicting the pandemic: sentiment evaluation and predictive analysis from large-scale tweets on Covid-19 by deep convolutional neural network

Predicting the pandemic: sentiment evaluation and predictive analysis from large-scale tweets on Covid-19 by deep convolutional neural network

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

Comparison of different machine learning techniques on location extraction by utilizing geo-tagged tweets: A case study

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