Election Prediction Based on Sentiment Analysis using Twitter Data

doi:10.5829/ije.2022.35.02b.13

Cited by 15 publications

(18 citation statements)

References 14 publications

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“…Using the aging estimation method with the proportion of positive message rate to negative messages rate, Yavari et al. ( 2022 ) designed an indicator of the election results in the future.…”

Section: Applications Of Sentiment Analysismentioning

confidence: 99%

Sentiment analysis: A survey on design framework, applications and future scopes

Bordoloi

Biswas

2023

Artif Intell Rev

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Sentiment analysis is a solution that enables the extraction of a summarized opinion or minute sentimental details regarding any topic or context from a voluminous source of data. Even though several research papers address various sentiment analysis methods, implementations, and algorithms, a paper that includes a thorough analysis of the process for developing an efficient sentiment analysis model is highly desirable. Various factors such as extraction of relevant sentimental words, proper classification of sentiments, dataset, data cleansing, etc. heavily influence the performance of a sentiment analysis model. This survey presents a systematic and in-depth knowledge of different techniques, algorithms, and other factors associated with designing an effective sentiment analysis model. The paper performs a critical assessment of different modules of a sentiment analysis framework while discussing various shortcomings associated with the existing methods or systems. The paper proposes potential multidisciplinary application areas of sentiment analysis based on the contents of data and provides prospective research directions.

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Section: Applications Of Sentiment Analysismentioning

confidence: 99%

Sentiment analysis: A survey on design framework, applications and future scopes

Bordoloi

Biswas

2023

Artif Intell Rev

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“…Therefore, many things can be analyzed through this platform. One of them is generating predictions in Twitter data analysis, which has attracted a lot of attention in recent years [18].…”

Section: Introductionmentioning

confidence: 99%

Sentiment Analysis on Marketplace in Indonesia using Support Vector Machine and Naïve Bayes Method

Dakwah,

Firdaus,

Furizal

et al. 2024

J. Ilm. Tek. Elektro Komput. Dan Inform

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This research addresses the challenges of marketplace customer feedback, which is an important aspect in today's era of online transactions. Marketplaces often receive many unsatisfactory comments from their customers through social media platforms. One approach that can be used to address this is sentiment analysis. This research contributes new insights as recommendations for marketplaces based on customer opinions on available services and delivery. The sentiment analysis methods used are Naive Bayes and Support Vector Machine because they are considered the best methods in training text-based classification models. Before being classified, the data goes through preprocessing stages such as cleaning, case folding, filtering, stemming, and tokenizing, as well as feature extraction stages using Term Frequency -Inverse Document Frequency (TF-IDF). The objects analyzed are divided into several well-known marketplaces in Indonesia such as Tokopedia, Lazada, and Shopee in discussing services and delivery of goods. The data used in this study comes from Twitter (X) social media accessed on August 27, 2023, using crawling techniques and successfully obtained as much as 2057 Tweet data. The best accuracy is obtained in the SVM method when compared to the Naive Bayes method. Words obtained based on service talks include price, service, application service, feedback, independence, and others. As for the delivery of goods, common words such as COD, delivery, package, courier, cheap, price, and others appear. Both methods used have good accuracy and can be recommended for use in similar research.

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“…Several popularity indices have been introduced in the literature (Tumasjan et al, 2011;Wang and Gan, 2017;Yavari et al, 2022). Their computation involves only the nature of the tweets, i.e.…”

Section: Introductionmentioning

confidence: 99%

Predicting election results: An improved popularity index

Kazadi

2023

SSRN Journal

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Election Prediction Based on Sentiment Analysis using Twitter Data

Cited by 15 publications

References 14 publications

Sentiment analysis: A survey on design framework, applications and future scopes

Sentiment analysis: A survey on design framework, applications and future scopes

Sentiment Analysis on Marketplace in Indonesia using Support Vector Machine and Naïve Bayes Method

Predicting election results: An improved popularity index

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