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

A Supervised Approach for Sentiment Analysis using Skipgrams and its Application to Sentiment Visualisation in Social Media

In this Ph.D. thesis we propose, as fundamental research, the design, development and evaluation of a supervised approach for sentiment analysis. This work is based on the hypothesis that an efficient use of the skipgram modelling can improve sentiment analysis tasks and reduce the resources they need. In summary, it consists on a supervised approach that uses machine learning techniques and skipgrams as information units, mainly focused on skipgram selection and filtering. This approach will be evaluated and compared to current state-of-the-art techniques. In addition, as applied research we propose a sentiment visualisation tool, strongly integrated with our sentiment analysis approach. This tool is oriented in the context of social media, measuring reputation and user interactions in real time.This research work has been partially funded by Generalitat Valenciana through project “SIIA: Tecnologías del lenguaje humano para una sociedad inclusiva, igualitaria, y accesible" with grant reference PROMETEU/2018/089, and by the Spanish Government and FEDER through the project RTI2018-094653-B-C22: “Modelang: Modeling the behavior of digital entities by Human Language Technologies" (“LIVING-LANG: Living Digital Entities by Human Language Technologies")

A Comparative Performance Evaluation of Algorithms for the Analysis and Recognition of Emotional Content

Sentiment Analysis is highly valuable in Natural Language Processing (NLP) across domains, processing and evaluating sentiment in text for emotional understanding. This technology has diverse applications, including social media monitoring, brand management, market research, and customer feedback analysis. Sentiment Analysis identifies positive, negative, or neutral sentiments, providing insights into decision-making, customer experiences, and business strategies. With advanced machine learning models like Transformers, Sentiment Analysis achieves remarkable progress in sentiment classification. These models capture nuances, context, and variations for more accurate results. In the digital age, Sentiment Analysis is indispensable for businesses, organizations, and researchers, offering deep insights into opinions, sentiments, and trends. It impacts customer service, reputation management, brand perception, market research, and social impact analysis. In the following experimental research, we will examine the Zero-Shot technique on pre-trained Transformers and observe that, depending on the Model we use, we can achieve up to 83% in terms of the model’s ability to distinguish between classes in this Sentiment Analysis problem

Measuring corporate reputation through online social media

What is corporate reputation? How can it be measured? These two questions have been widely discussed by academics, without coming to a shared definition or evaluation methodology. Each research gave its own corporate reputation definition, but all the studies agree on one point: corporate reputation is the result of the relationship between a company and its stakeholders. On the stakeholder’s opinion rely most of the corporate rep- utation measurement techniques that have been proposed during the past years, techniques that were criticized. In this work, we have investigated if corporate reputation can be evaluated from social media data We focused on the Volkswagen scandal and the buzz it created within the Twitter social network. VW’s scandal was chosen because its widely covered evolution through time and its broad effects on VW’s financial performance. In order to fulfill the research goal, tweets about VW (from 28/8/15 - to 6/6/16) were collected. This vast dataset was firstly analyzed and not VW’s related elements were re- moved. The remaining part of it was then classified in two main groups: tweets about VW, but not related to the scandal, and the ones that specifically referred to VW’s wrongdoing. Once the two sets were obtained, each of their elements were evaluated with a sentiment analysis software and after the opinion extraction was calculated the daily aggregated sentiment through a custom-built process, defined to adapt to the Twitter domain. This aggregation produced two different daily sentiment score: the general public opinion about VW and the judgement about the scandal. The work led to excellent results in the not-relevant elements removal phase and the classification one, but the opinion aggregation did not produce significant outcomes. This final results should not be considered as a research drawback, instead they represent a starting point for further analysis on the opinion creation process

Measuring Corporate Reputation Through Online Social Media: A Case Study of Volkswagen Scandal

Selle uurimistöö eesmärk on leida seos sotsiaalmeedias leviva avaliku arvamuse ning ettevõtete maine vahel. Püstitatud hüpoteesiks on see, et avalikud kommentaarid sotsiaalmeedias mõjutavad ettevõtete mainet. Uuringud on näidanud et sotsiaalmeedia kanalitel on mõju ettevõtete mainele. Mainet tunnustatakse üha enam ettevõtte väärtust mõjutava tegurina. See uurimistöö eeldab et ettevõtte majandustulemus on otsene maine indikaator. Sellest tulenevalt uurib see töö inimeste sotsiaalmeedias avaldatud kommentaaride mõju ettevõtte börsihinnale.See uurimistöö on Volkswageni skandaali juhtumiuuring ning keskendub Twitteri postitustele 2015 ja 2016 aastal. Eesmärgiks on leida kuidas avaliku arvamuse sentiment mõjutab ettevõtete mainet ning majanduslikke tulemusi kriisiolukorras. Protsessiks on valitud järgnev. Twitteri kasutajate arvamus Volkswageni kohta eraldatakse postitustest sentimendi numbri kujul. Seejärel leitakse korrelatsioon börsihinna, tehingute arvu ning leitud sentimendi vahel.Andmete valimiseks kasutati poolautomaatset lähenemist, mille abil eemaldati kaubanduslikud, poliitilised ning teised mitteseotud säutsud (tweet). Selle meetodi keskmiseks täpsuseks tuli kõrge 0.92. Edasisäutse (retweet) käsitletakse kui uusi säutse ning neid ei eemaldatud andmestikust et leida nende mõju korrelatsioonile. Sentimendi väärtuse leidmiseks kasutati 3 erinevat analüüsiviisi: "Microsoft Azure text analysis API", R-i pakett "Sentimentr" ning R-i pakett "SentimentAnalysis". Nende meetodite võrdlemisel leiti et Sentimentr-il on kõige parem korrelatsioon börsihinnaga.Korrelatsiooni tulemustest leiti, et säutsude sentimendi ja ettevõtete aktsiaturu andmete vahel on korrelatsioon. Päeva keskmisel sentimendil on kõige suurem negatiivne korrelatsioon (-0.84) aktsiaturu tehingute arvuga esimese kuu jooksul peale skandaali. Kuude möödudes korrelatsioon langeb järsult. Neljandal kuul peale kriisi on korrelatsioon langenud väärtuseni -0.27. See tähendab seda, et esimese kuu jooksul peale kriisi mida negatiivsemaks läheb sentiment, seda rohkem aktsiaid vahetatakse. Siiski see ei tähenda et negatiivne arvamus Twitteris mõjutab börsitehinguid. Korrelatsiooni tulemused näitavad et börsihind päeval D korreleerub paremini sentimendi väärtusega päeval D+4. See võib näidata, et tegelikult mõjutavad börsihinna kõikumised säutsude sentimenti. See läheb vastu esialgesele püstitatud hüpoteesile, kus väideti et sotsiaalmeedias leviv arvamus mõjutab ettevõtete mainet.This research investigates if there is any relationship between public opinion on social media and corporate reputation. The hypothesis of this study is that public comments on social media influences corporate reputation. Studies have shown that social media channels have an impact on corporate reputation. Reputation is increasingly recognized for its impact on value creation for corporations. This study assumes that the financial performance of a company is the direct indicator of its reputation. Therefore, this study investigates the influence of people's comments on social media on corporation stock market value.This research is a case study of the Volkswagen scandal and it focuses on data of Twitter posts between 2015 and 2016 and tries to find how sentiments from public opinion can influence corporate reputation and its financial performance in the crisis situation. The process is that the opinion of people on Twitter about Volkswagen is extracted from the tweets in the form of sentiment value. Then, the correlation between stock market price and volume and the sentiment of tweets is calculated.For the data selection, a semi-manual approach is used to remove commercial, political and unrelated tweets from the tweet data set. This approach shows a high average accuracy of 0.92. Retweets are treated as new tweets and are not removed from the dataset to find out the influence of retweets on the correlation. Then, three different sentiment analysis tools are used and compared to find out which one has more correlation with stock market price and volume of a corporation. These tools are "Microsoft Azure text analysis API", R package "Sentimentr" and R package "SentimentAnalysis". Comparing the resulting sentiments shows that Sentimentr tool has a higher correlation with stock market data.The correlation results show that there is a correlation between the sentiment of tweets and corporations' stock market data. The average sentiment of tweets per day has the highest negative correlation (-0.84) with the stock market volume of trades the first month after the scandal. As the months pass, the correlation drops dramatically (By the fourth month after the crisis, the correlation has dropped to -0.27). This means that first month after crisis while the average sentiment gets more negative, more stocks are traded. However, this doesn't necessarily indicate that negative opinion of people on Twitter influences the stock volume of trade. The correlation results show that stock price of day D has more correlation with the average sentiment of day D+4. This can indicate that actually, fluctuations in the stock price of the company can influence the sentiment of tweets. This is against our original hypothesis that public opinion on social media influences corporate reputation

Evolution of corporate reputation during an evolving controversy

Purpose: The purpose of this paper is to investigate the evolution of online sentiments toward a company (i.e. Chipotle) during a crisis, and the effects of corporate apology on those sentiments. Design/methodology/approach: Using a very large data set of tweets (i.e. over 2.6m) about Company A’s food poisoning case (2015–2016). This case was selected because it is widely known, drew attention from various stakeholders and had many dynamics (e.g. multiple outbreaks, and across different locations). This study employed a supervised machine learning approach. Its sentiment polarity classification and relevance classification consisted of five steps: sampling, labeling, tokenization, augmentation of semantic representation, and the training of supervised classifiers for relevance and sentiment prediction. Findings: The findings show that: the overall sentiment of tweets specific to the crisis was neutral; promotions and marketing communication may not be effective in converting negative sentiments to positive sentiments; a corporate crisis drew public attention and sparked public discussion on social media; while corporate apologies had a positive effect on sentiments, the effect did not last long, as the apologies did not remove public concerns about food safety; and some Twitter users exerted a significant influence on online sentiments through their popular tweets, which were heavily retweeted among Twitter users. Research limitations/implications: Even with multiple training sessions and the use of a voting procedure (i.e. when there was a discrepancy in the coding of a tweet), there were some tweets that could not be accurately coded for sentiment. Aspect-based sentiment analysis and deep learning algorithms can be used to address this limitation in future research. This analysis of the impact of Chipotle’s apologies on sentiment did not test for a direct relationship. Future research could use manual coding to include only specific responses to the corporate apology. There was a delay between the time social media users received the news and the time they responded to it. Time delay poses a challenge to the sentiment analysis of Twitter data, as it is difficult to interpret which peak corresponds with which incident/s. This study focused solely on Twitter, which is just one of several social media sites that had content about the crisis. Practical implications: First, companies should use social media as official corporate news channels and frequently update them with any developments about the crisis, and use them proactively. Second, companies in crisis should refrain from marketing efforts. Instead, they should focus on resolving the issue at hand and not attempt to regain a favorable relationship with stakeholders right away. Third, companies can leverage video, images and humor, as well as individuals with large online social networks to increase the reach and diffusion of their messages. Originality/value: This study is among the first to empirically investigate the dynamics of corporate reputation as it evolves during a crisis as well as the effects of corporate apology on online sentiments. It is also one of the few studies that employs sentiment analysis using a supervised machine learning method in the area of corporate reputation and communication management. In addition, it offers valuable insights to both researchers and practitioners who wish to utilize big data to understand the online perceptions and behaviors of stakeholders during a corporate crisis

Real-Time Customer Emotion Analysis in E-Commerce based on Social Media Data: Insights and Opportunities

In this era of social media, it's essential for businesses to monitor their customers options and feelings regarding their services and products in a timely manner. Due to the ease of sharing opinions and feedback on social media, the customers can share their reviews about the business or a product instantly. This feedback can have a significant impact on the business's reputation and in turn on its revenue. In this regard, sentiment analysis has developed into a vital tool that companies can use to comprehend the emotional factors that influence client behavior and to aid them in making decisions that will increase customer pleasure. This work presents the use of social media data for real-time consumer emotion analysis in e-commerce. The study aims to identify the most expressed emotions and provide businesses with the ability to tailor their product and services accordingly. The employed dataset consists of 58,000 English comments that have been labelled for 27 different emotion categories. The study uses machine learning methods to categorize the emotions expressed in the comments, including convolutional neural networks and Bidirectional Encoder Representations from Transformers (BERT). The practical result of this research shows the importance of machine learning model coupled with a user interface that can provide stakeholders, such as e-commerce companies, with insights into consumer emotion as well as realtime customer sentiment about their goods and services

Cancel Culture\u27s Impact on Brand Reputation

Cancel culture is becoming a relevant topic for public relations practitioners because of its negative impact on brand reputation. When brands do or support a wrongful action, consumers involved in cancel culture will use this as a reason to boycott a brand. This thesis aims to answer four research questions: (1) What are some key social media trends for brands that encounter cancel culture, (2) How do audiences, who engage in cancel culture, respond to brands that face cancel culture, (3) What do consumers\u27 emotional responses tell us about their relationship with the brand, and (4) What can brands learn from listening to consumers\u27 suggestive responses online. In this context, this thesis will focus on this definition of cancel culture: a new social media phenomenon aiming to boycott people, companies and systems for misaligning with social values. A case study on Goya Foods was used to answer these research questions along with a mixed-method approach. Focusing on the brand, social listening was conducted to learn about what consumers said about Goya in online conversations and identify their sentiment of the brand\u27s overall actions. This was followed by content and thematic analysis conducted on 200 Twitter tweets using various hashtags. The results revealed that the consumers held negative sentiment toward the brand as well as showed various negative emotions when engaging in the act of canceling the brand which led to Goya\u27s overall negative reputation.Based on these results, it is recommended for brands to take some sort of image restoration strategy to fix their reputation and relationship with their publics. It is also recommended for brands to listen to their publics\u27 responses to prevent further damage from cancel culture

El reto de vincular reputación online de destinos turísticos con competitividad

The aim of this study is to evidence how 2.0 conversations in social media impact the reputation of destinations. Additionally, the influence of co-creation practices is analysed. The five most competitive destinations worldwide have been chosen for the research. This paper demonstrates that monitoring social media is a challenge in tourism and is a strategic tool to support process decision making and for destination brand building in a sustainable way. Currently, there are several monitoring and analytic tools, but there is a lack of models to systematise and harness it for the Destination Management Organization (DMOs). In conclusion, how tourists play the main role in the competitiveness of Destinations with their experiences and opinions are considered, along with some keys for successful management of social media are given in the view of the results.info:eu-repo/semantics/publishedVersio

Using the Machine Learning Naive Bayes Algorithms for Sentiment Analysis on Online Product Reviews in the Air of Energy Optimization

The purpose of this study was to explore how consumers perceive two of the leading smartphone brands, Samsung and iPhone, using a corpus of tweets. Our approach involved sifting through the tweets to remove any irrelevant content, followed by a sentiment analysis to gain an overall perspective of how each brand was viewed. Our analysis demonstrated that Samsung received a higher proportion of tweets with negative sentiment as compared to iPhone. Moreover, the most common terms in tweets referring to Samsung reflected negative emotions like “concern,” “issue,” and “trouble,” while tweets about iPhone expressed positive emotions such as “like,” “great,” and “best.” These findings have significant implications for marketing research and offer valuable insights for businesses on how they can utilize social media to enhance their brand reputation and image