Using Social Network Analysis For Analysing The Acceptance of Islamic Mobile Banking In Indonesia

The purpose of the research is to find the positive and negative sentiment influencing the acceptance of Islamic Mobile Banking using Social Network Analysis. The research uses the reviews from Google Store from January 2017 to December 2018 related to the Bank Syariah Mandiri (BSM) and the Bank Muamalat. The total reviews for the research are 4472 (2489 for BSM and 1983 for Bank Muamalat). The result shows the positive sentiment for BSM mobile banking has 1407 nodes and 61537 edges. The positive sentiment of BSM mobile banking correlates with the feature of the transfer feature. For the negative sentiment, BSM mobile banking has 1811 nodes and 46926 edges which is related to an error when the customer checks the balance and transfer money. The positive sentiment of Muamalat mobile banking has 1020 nodes and 30492 nodes which correlate with the ease of use when customers use mobile banking. For the negative sentiment, Muamalat mobile banking has 531 nodes and 13691 edges which correlate with login and registration features

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

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

Semantic Sentiment Analysis of Twitter Data

Internet and the proliferation of smart mobile devices have changed the way information is created, shared, and spreads, e.g., microblogs such as Twitter, weblogs such as LiveJournal, social networks such as Facebook, and instant messengers such as Skype and WhatsApp are now commonly used to share thoughts and opinions about anything in the surrounding world. This has resulted in the proliferation of social media content, thus creating new opportunities to study public opinion at a scale that was never possible before. Naturally, this abundance of data has quickly attracted business and research interest from various fields including marketing, political science, and social studies, among many others, which are interested in questions like these: Do people like the new Apple Watch? Do Americans support ObamaCare? How do Scottish feel about the Brexit? Answering these questions requires studying the sentiment of opinions people express in social media, which has given rise to the fast growth of the field of sentiment analysis in social media, with Twitter being especially popular for research due to its scale, representativeness, variety of topics discussed, as well as ease of public access to its messages. Here we present an overview of work on sentiment analysis on Twitter.Comment: Microblog sentiment analysis; Twitter opinion mining; In the Encyclopedia on Social Network Analysis and Mining (ESNAM), Second edition. 201

Social Sentiment and Stock Trading via Mobile Phones

What happens when uninformed investors trade stocks via mobile phones? Do they react to social sentiment differently than more informed traders in traditional trading? Based on 16,817 data observations and econometric analysis for the trading of 251 equities in Korea over 39 days, we present evidence of herding behavior among uninformed traders in the mobile channel. The results indicate that mobile traders seem more easily swayed by changing social sentiment. In addition, stock trading in the traditional channel probably influences sentiment formation in the market overall. Mobile traders follow signals in social media suggesting that they engage in less beneficial herding behavior, based on evidence that we obtained for the occurrence of more negative feedback trading. This allows us to offer a new interpretation of how mobile channel stock trading works, and open a new portal for analytics with digital data related to the trading behavior of different investors

Mobile Informed trading leveraging social sentiment

Past works exploring the relationship between social sentiment and stock markets have been of great interest to investors and scholars across multiple disciplines. In this study, we debate whether informed trading is practically connected with social media information even though uninformed trading is commonly linked with social sentiment. We measure the probability of informed trading and perform analysis of covariance on a data set classifying firm cohorts on two trading channels, traditional and mobile. The results show that the influence of positive sentiment on informed trading is statistically significant for well-known firm group on the mobile channel. However, negative sentiment and other factors do not affect the informed trading in the same setting. This implies that social media is likely to be a channel for mobile informed trading, which is different from previous research. This study offers new insights into the economic impact of social media on the informed trading

Improving sentiment analysis on PeduliLindungi comments: a comparative study with CNN-Word2Vec and integrated negation handling

This study investigates sentiment analysis in Google Play reviews of the PeduliLindungi application, focusing on the integration of negation handling into text preprocessing and comparing the effectiveness of two prominent methods: CNN-Word2Vec CBOW and CNN-Word2Vec SkipGram. Through a meticulous methodology, negation handling is incorporated into the preprocessing phase to enhance sentiment analysis. The results demonstrate a noteworthy improvement in accuracy for both methods with the inclusion of negation handling, with CNN-Word2Vec SkipGram emerging as the superior performer, achieving an impressive 76.2% accuracy rate. Leveraging a dataset comprising 13,567 comments, this research introduces a novel approach by emphasizing the significance of negation handling in sentiment analysis. The study not only contributes valuable insights into the optimization of sentiment analysis processes but also provides practical considerations for refining methodologies, particularly in the context of mobile application reviews