Twitter Based Outcome Predictions of 2019 Indian General Elections Using Decision Tree

Joseph, Ferdin Joe John

doi:10.1109/incit.2019.8911975

Cited by 25 publications

(8 citation statements)

References 7 publications

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“…Once the RNN was trained, it was evaluated with test data resulting in an 86.1% prediction rate. In 2019, a new methodology was presented in Joseph (2019) , which predicted Indian general elections using a decision tree. Ruling and opposing parties’ data was gathered from Twitter.…”

Section: Methodsmentioning

confidence: 99%

On the frontiers of Twitter data and sentiment analysis in election prediction: a review

Alvi,

Ali,

Ahmed

et al. 2023

PeerJ Computer Science

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Election prediction using sentiment analysis is a rapidly growing field that utilizes natural language processing and machine learning techniques to predict the outcome of political elections by analyzing the sentiment of online conversations and news articles. Sentiment analysis, or opinion mining, involves using text analysis to identify and extract subjective information from text data sources. In the context of election prediction, sentiment analysis can be used to gauge public opinion and predict the likely winner of an election. Significant progress has been made in election prediction in the last two decades. Yet, it becomes easier to have its comprehensive view if it has been appropriately classified approach-wise, citation-wise, and technology-wise. The main objective of this article is to examine and consolidate the progress made in research about election prediction using Twitter data. The aim is to provide a comprehensive overview of the current state-of-the-art practices in this field while identifying potential avenues for further research and exploration.

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Section: Methodsmentioning

confidence: 99%

On the frontiers of Twitter data and sentiment analysis in election prediction: a review

Alvi,

Ali,

Ahmed

et al. 2023

PeerJ Computer Science

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“…Joseph, [13] used a decision tree classifier for predicting the outcomes of the Indian general election in 2019 by utilizing the sentiment analysis of the Twitter data. The experimental results demonstrated that the decision tree classifier was effective in mapping the sentiments of people across several phases of polls.…”

Section: Related Workmentioning

confidence: 99%

“…The comparative results of the prior models and the proposed EDOA-DBN model are stated in table 6. Joseph, [13] integrated Bag of words and TF-IDF techniques to extract features from the input data. The extracted features were classified by utilizing many machine learning classifiers such as SVM, decision tree, Naïve Bayes and random forest.…”

Section: Comparative Analysismentioning

confidence: 99%

Effective Twitter Sentiment Analysis Using Deep Belief Network with Enhanced Dragonfly Optimization Algorithm

2023

IJIES

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In this research article, an effective model is implemented for predicting the outcome of the Indian general election 2019 and farmer's protest by utilizing the sentiment analysis of Twitter data. In the initial segment, the raw Twitter data are acquired from the Indian Political tweets 2019 and farmers protest tweets databases. Further, the denoising operations such as removal of unnecessary space, tabs, new-line, hashtag symbols, non-English characters, punctuations, numbers and special characters are used to enhance the quality of the acquired data. Then, the keyword trend analysis and topic modeling utilizing latent dirichlet allocations (LDAs) are performed for better data representation. Next, the extraction of the feature is carried out utilizing skip-gram and term frequency-document level frequency (TF-IDF) techniques, and further, the feature optimization is accomplished using enhanced dragonfly optimization algorithm (EDOA) for selecting the optimal feature vectors. In the EDOA, a Brownian motion is added for improving the probabilistic behaviors. Lastly, the selected features are given to the deep belief network (DBN) model to classify the people's sentiments into negative, neutral, and positive classes. The experimental evaluations demonstrated that the EDOA-DBN model has obtained 99.22% and 98.83% of accuracy on both Indian political tweets 2019 and farmers protest tweets databases, which are maximum related to other existing models.

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“…Machine learning classifiers use supervised approach and need training examples which can be labeled manually or obtained from online sources. naive bayes (NB) [37], support vector machines [38], [39], decision tree [40], [41], AdaBoost, regression logistic regression, J48, Simple CART, random tree are some commonly used machine learning based classifiers. Kolchyna et al [36] analyzed various machine learning classifiers.…”

Section: Machine Learning Classification Techniquesmentioning

confidence: 99%

Analyzing impact of number of features on efficiency of hybrid model of lexicon and stack based ensemble classifier for twitter sentiment analysis using WEKA tool

Rani¹,

Gill²,

Gulia³

2021

IJEECS

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<span>Twitter is used by millions of people across the world, so the data collected from Twitter can be highly valuable for research and helpful in decision support. Here in this paper ‘Twitter US Airline data’ from Kaggle data repository is used for sentiment classification of customers’ reviews. The current research aims to implement various machine learning classifiers, Stack-based ensemble classifiers and hybrid of lexicon classifier with other classifiers. 11 different classification models are implemented for different sized feature sets. Also, all the 11 models are re-implemented by adding sentiment score of lexicon based classifier as one of the features in the feature set. Results are analyzed by varying number of input feature variables used in the classification. Four different size feature sets having 301,501, 701, and 1301 number of features are used to analyze the variations in the final findings. Chi-Square and Information gain techniques are used for feature selection. The results show that an increase in the number of features increases the accuracy up to 701 features. After that, accuracy is stable or decreases with increase in feature set size. Also, the cost of adding sentiment score of lexicon classifier to the input feature set is nominal, but the results are improved consistently. WEKA and R Studio tools are used for analysis and implementation. Accuracy and Kappa are used for representing and comparing the efficiency of models.</span>

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Twitter Based Outcome Predictions of 2019 Indian General Elections Using Decision Tree

Cited by 25 publications

References 7 publications

On the frontiers of Twitter data and sentiment analysis in election prediction: a review

On the frontiers of Twitter data and sentiment analysis in election prediction: a review

Effective Twitter Sentiment Analysis Using Deep Belief Network with Enhanced Dragonfly Optimization Algorithm

Analyzing impact of number of features on efficiency of hybrid model of lexicon and stack based ensemble classifier for twitter sentiment analysis using WEKA tool

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