Sentimen Analisis pada Data Tweet Pengguna Twitter Terhadap Produk Penjualan Toko Online Menggunakan Metode K-Means

In this big data era, the use of social media often makes posts in his social media accounts in the form of opinions on events and things around him. One of them is making a post that gives an opinion on the events and items around it. One of them is making a post that gives an opinion on an item that has just been purchased, so that the effect is on other users who are connected to it. The more people who know it, then indirectly people will get to know the item. For that from the description of the problem above, this study raises an idea to make an analysis of social media sentiment which aims to provide a decision of consumer opinion on social media on sales products. As for the several stages of the method for the research, namely from the collection of data carried out by collecting existing data in tweets from social media Twitter using the R programming language. The data produces raw or raw data associated with sales items. With the K-means method as inputting, after each group is known from the K-Means outpu

Discovering Patterns in Textual Data Using SAS Visual Analytic

Today's generation are more tech savvy than previous generations. They tend to complete their everyday tasks from making their daily schedule to purchasing their daily necessities on the internet. Due to the boom in this culture, e-commerce retail stores have increased their retail sales. According to the United States of America’s Census Bureau, the retail online sales in the year 2012 has peaked at $45.6 billion from the year 2001. This is an increase of 26.9%. This proves that the digital economy is growing and will continue to grow further. In e-commerce platforms there will definitely be a large requirement for logistics which develop a cross organisational support between supply chain management and retail sales. Using text analysis an in depth review of understanding customer satisfaction towards logistical issues to further enhance product delivery and logistical improvements in terms of logistics operations. Mainly using sentiment analysis. Challenges to product delivery are discussed and viable solutions to overcome current or existing logistical issues are presented in this paper

Deciphering Voice of the Customer using Text Analytics and Sentiment Analysis: An Interpretable Review Rating Prediction using RoBERTa

In this era of cut-throat competition among traditional and newer digital organizations, capturing, listening, and understanding customer voices are critical for success in the marketplace. The challenge to decipher the voice of the customer (VOC) has multiplied many times today, as now the number of customer reviews are present in multiple  platforms and the data to be analyzed is huge. Sentiment analysis, and text analytics using machine learning, deep  learning tools and transformer-based tools can be applied to gather meaningful insights from these data. This paper applies the traditional machine learning tools of the Naive Bayes classifier, Random Forest and AdaBoost to predict  the customer review ratings. These results are compared with deep learning methods of CNN, RNN and Bi-LSTM and  transformer-based approaches of BERT, DistilBERT and RoBERTa. The results show that RoBERTa has the highest accuracy among these methods. Paper also uses the explainable AI tool of LIME to understand the customer sentiments deeper in terms of why customers are giving a particular rating to the product. Business organizations will continue to use more and more AI tools to understand the customer feedback and the attempt in this paper is to learn how we can make predictions faster and more accurately

ANALISIS SENTIMEN PADA ULASAN APLIKASI MOBILE BANKING MENGGUNAKAN METODE NAÏVE BAYES DENGAN KAMUS INSET

Perkembangan teknologi informasi dan komunikasi berpengaruh pada partisipasi dan interaksi pengguna dalam online platform, seperti contohnya adalah pemberian ulasan pada sebuah aplikasi di Google Play Store. Bagi perusahaan, ulasan dapat digunakan untuk mengetahui kebutuhan pengguna dan dapat digunakan juga sebagai bahan evaluasi untuk mengembangkan dan memperbaiki aplikasinya, namun terkadang dalam sebuah ulasan terdapat ketidaksesuaian antara isi ulasan dengan pemberian rating pada ulasan, yang berarti bahwa sentimen dari sebuah ulasan tidak dapat dilihat dari jumlah rating pada ulasan. Dalam sebuah ulasan juga dapat terjadi kesalahan penulisan baik disengaja ataupun tidak disengaja yang membuat ulasan tersebut susah untuk dibaca dan dimengerti. Pada penelitian ini dilakukan analisis sentimen dengan studi kasus aplikasi Victoria Mobile Banking untuk mengklasifikasikan ulasan aplikasi kedalam ulasan yang bersifat positif atau negatif serta dapat memberi visualisasi hasil analisis ulasan. Proses klasifikasi pada sistem menggunakan metode Naïve Bayes. Data latih yang digunakan adalah data ulasan yang telah diberi label secara otomatis menggunakan kamus InSet yang telah melalui penyesuaian kata dan bobot. Algoritma klasifikasi tersebut diuji menggunakan confusion matrix dan menghasilkan nilai precision 90,4%, recall 100%, f-measure 95%, dan akurasi 93,1%

Sentiment Analysis for Online Product Reviews and Recommendation Using Deep Learning Based Optimization Algorithm

Recently, online shopping is becoming a popular means for users to buy and consume with the advances in Internet technologies. Satisfaction of users could be efficiently improvised by carrying out a Sentiment Analysis (SA) of larger amount of user reviews on e-commerce platform. But still, it is a challenge to envision the precise sentiment polarity of the user reviews due to the modifications in sequence length, complicated logic, and textual order. In this study, we propose a Hybrid-Flash Butterfly Optimization with Deep Learning based Sentiment Analysis (HFBO-DLSA) for Online Product Reviews. The presented HFBO-DLSA technique mainly aims to determine the nature of sentiments based on online product reviews. For accomplishing this, the presented HFBO-DLSA technique applies data pre-processing at the preliminary stage to make it compatible. Besides, the HFBO-DLSA model uses deep belief network (DBN) model for classification. The HFBO algorithm is used as a hyperparameter tuning process to improve the SA performance of the DBN method. The experimental validation of the presented HFBO-DLSA method has been tested under a set of datasets. The experimental results reveal that the HFBO-DLSA approach surpasses recent techniques in terms of SA outcomes. Specifically, when compared to various existing models on the Canon dataset, the HFBO-DLSA technique achieves remarkable results with an accuracy of 97.66%, precision of 98.54%, recall of 94.64%, and an F-score of 96.43%. In comparative analysis, other approaches such as ACO, SVM, and NN exhibit poorer performance, while TextCNN, BiLSTM, and RCNN approaches yield slightly improved SA results

Festive or Failure? Differences in Sentiments of Consumer Tweets on Amazon Festive Flash Sale before and after COVID-19

Amazon’s Great Indian Sale is an annual mega shopping festival and provides an opportunity for retailers selling on e-commerce platforms to increase their sales, traffic as well as visibility. This study aims to understand consumer behaviour through the analysis of user-generated content (UGC) on social media concerning Amazon’s Great Indian Sale from 2015 to 2020. This time period was especially significant since it allowed for the analysis of differences caused by the pandemic. It was further divided into pre-COVID-19, during  COVID-19 and post-COVID-19 period. The study also aims to establish a causal relationship between the feelings of engagement of the UGC on Twitter, with the strategies of marketing and promotion of the companies. Twitter’s API was used to extract the UGC. A two-step methodology was used to extract meaningful insights. First, Latent Dirichlet Allocation (LDA) was used to identify major topics related to the Flash sales organized during the festive season. Next, Sentiment Analysis was used to classify each tweet into Positive, Negative and Neutral Sentiments in order to understand user opinions and feelings towards the marketing campaign. It was found that both during the COVID period as well as the post COVID period, topics like Deals & Offers and Exclusive Promotions were positively perceived by consumers. Topics like Pre-event Excitement, Diwali Shopping Experience, Customer Support, and Money-saving Opportunities generated positive feelings during the pre-COVID times. During COVID times, positive sentiments were generated by topics like Delivery Workers and Attractive Deals for Gifting. On the other hand, topics like Insults and Noise generated negative sentiments in both the COVID and post COVID periods. The findings of this study are significant not only to improve and devise marketing strategies and social media content for E-commerce companies, but also to provide an understanding of the effectiveness of festive sales

Ferramentas de gestão linguística na internacionalização do negócio eletrónico

Numa era em que os negócios se internacionalizam a nível global, as empresas que pretendam ter sucesso no mercado competitivo da atual economia digital têm de ser capazes de identificar e ultrapassar as barreiras que se apresentam ao negócio eletrónico.. A presente dissertação foca-se na língua como fator que condiciona a experiência do utilizador digital no processo de compra online. Efetivamente cada vez mais o utilizador que acede a um site com o intuito de comprar é multilingue e prefere pesquisar e adquirir um produto/serviço na língua materna, pelo que a língua é um aspeto a ter em consideração desde o primeiro momento que o consumidor estabelece contacto com o produto. Assim, tem-se assistido gradualmente a um despertar de consciência para a necessidade de localizar a oferta online, disponibilizando contéudo traduzido e culturalmente adaptado tendo em conta as especificidades do mercado ao qual se pretende expandir. A tradução automática, associada à inteligência artificial, tem sido uma das apostas por parte de muitos negócios online para ultrapassar a questão da língua, (quando não comum entre duas partes que comunicam) como barreira ao negócio eletrónico. Este estudo apresenta uma revisão de literatura sistemática que pretende explorar se a língua é, efetivamente, um fator importante influenciador na compra online através de uma plataforma de comércio eletrónico. Simultaneamente, a discussão extende-se a ferramentas artificiais de gestão linguística, nomedamente à tendência de localizar e traduzir automaticamente o produto/serviço online e de disponiblizar assistentes virtuais que, recorrendo aos avanços na área de Natural Language Processing (NLP), melhoram significativamente a experiência do utilizador, aumentando, assim, a satisfação do mesmo.In an era when business is internationalized globally, companies that wish to succeed in the competitive market of the current digital economy must be able to identify and overcome the barriers that present themselves to e-business. This dissertation focuses on language as a factor that conditions the digital user's experience in the online shopping process. Effectively, the user who accesses a website with the intention of buying is progressively multilingual and prefers to research and purchase a product / service in the mother tongue, so the language is an aspect to be taken into account from the first moment that the consumer establishes contact with the product. Thus, there has been a gradual awakening of awareness towards the need to locate the offer online, providing translated and culturally adapted content taking into account the specificities of the targeted market. Automatic translation, related to artificial intelligence,, has been one of the investments on behalf of many online businesses to overcome the issue of language (when not common between two communicating parties) as a barrier to electronic business. This study presents a systematic literature review that aims to explore whether language is, in fact, an important influencing factor in online shopping through an e-commerce platform. Simultaneously, the discussion extends to artificial language management tools, namely the tendency to automatically locate and translate the product / service online and to make virtual assistants available, which, using advances in the area of Natural Language Processing (NLP), shall improve significantly the user experience, thereby increasing user satisfaction

Exploring how Interactions and Responses within the Servicescape combine to form Customer Experience – A Text Mining Approach

The core research objective in this thesis is to address the ways in which Customer Experience (CX) emerges through the combinational effects of multiple customer interactions at touchpoints and their resulting CX responses. The empirical study designed in this work is positioned to build upon existing literature within the Service Management field. According to extant work, CX can be viewed from both the provider’s perspective (e.g. ‘intended’ or designed), and the customer’s perspective (e.g. ‘realised’ or subjective). The thesis integrates both accounts through the presentation of a new conceptual model which forms the basis for the design of the empirical study. Several limitations are addressed in this work. First, building on the notion that CX emerges across multiple touchpoint interactions (Lemon and Verhoef, 2016) the study explores the impact multiple interaction types, and their associated responses, have on overall CX. Extant studies have tended to view CX at single touchpoint interactions (Becker and Jaakkola, 2020). CX emerges across multiple touchpoint interactions, which each induce responses in the customer. As it stands, little is known about how this process occurs, or the relationships which exist between customer interaction, customer response, and overall CX. Second, the study widens the field and its understanding of the servicescape from an ‘unbounded’ perspective (Rosenbaum and Massiah, 2011). Traditionally, studies have explored CX through the impact of provider-owned touchpoints, predominantly within bounded service sites. The study addresses requests to explore the impact of wider, non-provider-controlled touchpoints on overall CX (Kandampully et al., 2018). Relating to this aim, very little existing work deals with the impact of natural servicescape touchpoints on CX. The case studies in this work have been chosen for their suitability to address this gap. The study employs a comparative case study approach from the cultural heritage sector. Text mining (TM) and text analytics (TA) techniques are employed to capture and assess CX elements found within customer feedback data from an online review depository. Contrary to existing work in this field, the study employs a three-step annotation process to concept classification which can ensure rigour in the results. The purpose of the analysis process is to capture patterns of CX responses and customer interactions within the data and assess their relationship to overall CX ratings. Both quantitative measures (e.g. statistical analysis) and qualitative measures (e.g. verbatim text analysis) are used to explore a number of key questions relating to the core research objective. The empirical study performed in this work results in several key findings. The study finds that CX arises as a combination of customer interactions and CX responses, with each pattern impacting the overall experience in different ways. Results suggest that pattern prevalence and prominence are not core drivers of customer rating, but rather that significance measures need to be employed. From a customer perspective, negative CX responses have a stronger effect on overall CX rating than positive responses. These can be induced through touchpoint interactions beyond the control of the provider. The emotional content of the experience is key, with customer surprise, anger, and sadness significantly impacting CX to a greater degree than other discrete emotions. The findings suggest that customer expectations play an important role in the delivery of CX. Customer expectations can be used to make sense of the differences in terms of patterns and the statistical significance of their relationship to CX rating. Several potential avenues for future work to further develop these themes are put forward in the final stages of the thesis.EPSRC and University of Exeter Business Schoo