Twitter Sentiment to Analyze Net Brand Reputation of Mobile Phone Providers

Vidya, Nur Azizah; Fanany, Mohamad Ivan; Budi, Indra

doi:10.1016/j.procs.2015.12.159

Cited by 87 publications

(37 citation statements)

References 4 publications

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“…Sentiment analysis will be using the SVM method because based on Vidya et.al. research SVM is the best clasifier model compared to naïve bayes and decision trees [14]. The results of sentiment analysis produce a confusion matrix which is used to determine the accuracy, recall, and precision of the classification method used.…”

Section: Sentiment Analysismentioning

confidence: 99%

“…This research uses the SVM method based on the study of Vidya et.al. which states that SVM method has the best performance compared to Naïve Bayes and decision trees [14]. And to determine the best ratio, accuracy is used as the basis for determining the performance of the model [32].…”

Section: Sentiment Analysismentioning

confidence: 99%

“…This research also using Vidya et.al. findings about comparation Naïve Bayes, Support Vector Machines, and Decision Tree model, as the metric for net brand reputation in social media and concluded SVM as the best technique [14]. Therefore, this research uses SVM to process the data.…”

Section: Introductionmentioning

confidence: 96%

“…To utilize SIM Score, conversation data crawled from Twitter must be classified into positive and negative sentiments. This study use Support 368 Vector Machine classification model to predict those data as positive or negative sentiment [14]. The results of the metrics calculation and sentiment analysis can help in optimizing corporate communication on social media, which is an important role to achieve a stable audience growth and can generate monetary profit [15].…”

Section: Introductionmentioning

confidence: 99%

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Comparison Analysis Of Social Influence Marketing For Mobile Payment Using Support Vector Machine

Prihono

Sari

2019

KINETIK

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There are many digital-based financial services today, one of them is mobile payment service. Users can deposit money and make online transaction with their smartphone through mobile application. Five mobile payment service providers with the most users in Indonesia, according to Dailysocial are GOPAY, OVO, LinkAja, DANA, and PayTren. This study uses sentiment analysis to classify user’s opinion into positive and negative classes. The classification method used is Support Vector Machine. This study utilizes three metrics, namely Net Sentiment, Share of Voice, and Social Influence Marketing Score. Those metrics are useful for knowing reputation, reach, and influence of brands in social media. The findings in this study indicate that GOPAY, OVO, DANA, and PayTren have a positive dominant sentiment, while LinkAja has a negative dominant sentiment. The brand with the biggest influence and reaches in the mobile payment industry is GOPAY. While the highest reputation brand is PayTren. The implication of this research is to encourage mobile payment providers to be able to monitor their brand conditions among their competitors by utilizing social network analysis method.

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Section: Sentiment Analysismentioning

confidence: 99%

Section: Sentiment Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 96%

Section: Introductionmentioning

confidence: 99%

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Comparison Analysis Of Social Influence Marketing For Mobile Payment Using Support Vector Machine

Prihono

Sari

2019

KINETIK

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“…The next direction is sentiment analysis, when one would like to figure out people's attitude to a certain subject, for example, regarding mobile phone providers [8]. Another paper [9] studied the sentiment analysis in e-learning, and in the sentiment detection part they combined two approaches: machine learning approach and lexicon-based approach.…”

Section: Literature Reviewmentioning

confidence: 99%

Extracting Patent-Related Information from Online Social Networks: Case of Facebook

Ivanov¹,

Tekić²

2017

DAAAM Proceedings

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In this study, we investigate how to identify and extract patent-related information from Facebook, the largest online social network. In the first step, we identified a list of trustworthy sources we started search from. Then, we developed algorithms for extracting and filtering information, and based on them software tool that is able to identify and deliver a patent, the Facebook post where it is mentioned and news / blog article which discuss it. We did a pilot test and collected more than 50 examples of articles (and Facebook posts) that add value to the patent they refer to. We classified collected articles and discussed how they can be used. Finally, we outlined where developed tool can be applied.

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Online media sentiment analysis for US oysters

Bradford,

Quagrainie

2024

Aquaculture Fish & Fisheries

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Sentiment analysis, a form of data analytics, utilises information from online discussions, reviews, and social media posts to assess consumer sentiments. This study utilised data collected from social media using online listening procedures to assess online sentiments on oysters from January 2019 through December 2022. The analysis utilises machine learning algorithms to extract consumer sentiments, opinions, and demands from online chatter from different online domains. The online sentiments are determined as positive, negative, or neutral based on their word choice, tone, and context. The information provided gives insights into perception, which is valuable information for oyster producers, seafood industry stakeholders, and marketers to identify consumer preferences and formulate appropriate strategies accordingly. The results suggest that while farmed oysters are gaining popularity, there are still some concerns and criticisms around the industry. Positive words associated with mentions of oysters in general include ‘great’, ‘love’, ‘delicious’, ‘enjoyed’, and ‘oyster bar’, while negative words associated with oysters include ‘water’, ‘raw oyster’, ‘bad’, and ‘not eat’. The overall percentage net sentiment associated with all oysters in the United States is positive at 63%. The net sentiment associated with wild oysters is positive, at 51%, and that of farmed oysters is 58%. The oyster industry could invest more in public education, sustainability, and water‐cleaning initiatives to improve its image. Utilising social media to monitor and shape public perception can help the industry address concerns and enhance oyster‐related sentiments, offering valuable insights for marketing and sales strategies.

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Twitter Sentiment to Analyze Net Brand Reputation of Mobile Phone Providers

Cited by 87 publications

References 4 publications

Comparison Analysis Of Social Influence Marketing For Mobile Payment Using Support Vector Machine

Comparison Analysis Of Social Influence Marketing For Mobile Payment Using Support Vector Machine

Extracting Patent-Related Information from Online Social Networks: Case of Facebook

Online media sentiment analysis for US oysters

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