Sentiment analysis of financial Twitter posts on Twitter with the machine learning classifiers

Cam, Handan; Cam, Alper Veli; Demirel, Ugur; Ahmed, Sana

doi:10.1016/j.heliyon.2023.e23784

Cited by 8 publications

(8 citation statements)

References 61 publications

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“…While on the application of the SVM method, A study on sentiment analysis of market predictions in order to find out Tweets about finance in Turkish. SVM as one of the methods used gets an accuracy result of 89% [62]. A study revealed to use of linguistic rules-based feature selection methods in tourism reviews.…”

Section: Discussionmentioning

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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Section: Discussionmentioning

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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“…Data collection is the process of obtaining tweet datasets by utilizing the API (Application Program Interface) from Twitter [12]. The tool used for this data collection is Google Collab with the Python programming language [6], with a set limit of 3000 data. Data collection was carried out from 12 November 2023 to 11 December 2023, during the TikTok Shop closure period in Indonesia, and 2903 data was obtained.…”

Section: A Data Collectionmentioning

confidence: 99%

“…Data labeling in this research is done manually. Compared to machine labeling [13], this manual technique can provide accurate results without needing to see many examples, but it involves a lot of work by humans because it requires humans to read and analyze each dataset before labeling positive and negative [6], [7] .…”

Section: B Data Labelingmentioning

confidence: 99%

“…After the preprocessing is complete [6], [7], [11], the dataset will be transformed into vector form using RapidMiner with the "Process Document from Data" feature, selecting the TF-IDF vector creation parameter. Various algorithms can be applied to the dataset once it has been transformed into vector form [7].…”

Section: Tf-idf Vectorizermentioning

confidence: 99%

“…Sentiment analysis research was conducted by [6], using a dataset from Twitter as large as 17189, with Support Vector Machine (SVM) producing an accuracy rate of 0.89% and AUC of 0.8729. The study also used a Decision Tree which resulted in an accuracy rate of 0.81% and AUC of 0.8070.…”

Section: Introductionmentioning

confidence: 99%

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Sentiment Analysis of TikTok Shop Closure in Indonesia on Twitter Using Supervised Machine Learning

Al Habesyah,

Herteno,

Indriani

et al. 2024

j.electron.electromedical.eng.med.inform

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TikTok Shop is one of the features in TikTok application which facilitates users to buy and sell products. The integration of TikTok Shop with social media has provided new opportunities to reach customers and increase sales. However, the closure of TikTok Shop has caused controversy among the public. This study aims to analyze the views and responses of TikTok users in Indonesia to the closure of TikTok Shop. The dataset used was obtained from Twitter. The research methodology consists of labeling, oversampling, splitting, and machine learning, which includes SVM, Random Forest, Decision Tree, and Deep Learning (H2O). The contribution of this research enriches our understanding of the implementation of machine learning, especially in sentiment analysis of TikTok Shop closures. From the test results, it is known that Deep Learning (H2O) + SMOTE obtained AUC 0.900, without using SMOTE, AUC 0.867. SVM + SMOTE obtained AUC 0.885, without using SMOTE AUC 0.881. Random Forest + SMOTE obtained AUC 0.822, while without using SMOTE AUC 0.830. Decision Tree + SMOTE AUC 0.59; without SMOTE, AUC 0.646. Deep Learning (H2O) with SMOTE produces better performance compared to SVM, Random Forest, and Decision Tree. With an AUC of 0.900; it can be said that Deep Learning (H2O) has excellent performance for sentiment analysis of TikTok Shop closures. This research has significant implications for social electronic commerce due to its potential utilization by social media analysts.

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A hybrid machine learning model for sentiment analysis and satisfaction assessment with Turkish universities using Twitter data

Alawi,

Bozkurt

2024

Decision Analytics Journal

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Sentiment analysis of financial Twitter posts on Twitter with the machine learning classifiers

Cited by 8 publications

References 61 publications

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

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

Sentiment Analysis of TikTok Shop Closure in Indonesia on Twitter Using Supervised Machine Learning

A hybrid machine learning model for sentiment analysis and satisfaction assessment with Turkish universities using Twitter data

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