A Survey on Mining Top-k Competitors from Large Unstructured Dataset Using k_means Clustering Algorithm and Sentiment Analysis Approach

Along line of research has shown the vital significance of recognizing and observing company�s contestants. In the framework of this activity various questions are emerge like: In what way we formalize and measure the competitiveness between two items? Who are the most important competitors of a specified item? What are the various features of an item that act on competitiveness? Inspired by this issue, the advertising and administration group have concentrated on observational strategies for competitor distinguishing proof and in addition on techniques for examining known contenders. Surviving examination on the previous has concentrated on mining near articulations (e.g.one product is superior then other product) from the web or other documentary sources. Despite the fact that such articulations can without a doubt be indications of strength, they are truant in numerous spaces. By surveying the various papers, we found the conclusion of basic significance of the competitiveness between two items on the basis of market segments

Harnessing the power of the general public for crowdsourced business intelligence: a survey

International audienceCrowdsourced business intelligence (CrowdBI), which leverages the crowdsourced user-generated data to extract useful knowledge about business and create marketing intelligence to excel in the business environment, has become a surging research topic in recent years. Compared with the traditional business intelligence that is based on the firm-owned data and survey data, CrowdBI faces numerous unique issues, such as customer behavior analysis, brand tracking, and product improvement, demand forecasting and trend analysis, competitive intelligence, business popularity analysis and site recommendation, and urban commercial analysis. This paper first characterizes the concept model and unique features and presents a generic framework for CrowdBI. It also investigates novel application areas as well as the key challenges and techniques of CrowdBI. Furthermore, we make discussions about the future research directions of CrowdBI

Literature review - Twitter as A Tool of Market Intelligence for Businesses: Sentiment analysis approach

Purpose As an emerging technology, sentiment analysis of Twitter has aroused interest in the field of business research. The thesis has three primary objectives. The first objective is to identify how businesses could utilize sentiment analysis of Twitter in their market intelligence functions. The second is to determine how sentiment analysis of Twitter compares to more traditional methods of market intelligence. Thirdly, this thesis aspires to bring technology-oriented discipline easier to approach for business researchers. Methodology The research method of this thesis is a literature review. The thesis revises prior published and peer-reviewed articles with a focus on sentiment analysis of Twitter and its applications to market intelligence. Findings There are three significant findings in this thesis. 1. Companies have utilized sentiment analysis for various purposes of market intelligence with encouraging results. 2. Sentiment analysis of Twitter has a variety of similarities with traditional market intelligence methods. In the future, it will be an auspicious technique for market intelligence as its accuracy is improved, and companies utilize it more frequently for practical purposes. 3. Even though Twitter sentiment analysis has raised plenty of interest, there is no clear research field within the business, and more specifically, market intelligence related literature. Future research For future research, this thesis provides a review of the possibilities and uses of Twitter sentiment analysis in the context of market intelligence. Its focus is to support especially business research. Reviewed literature illustrates that there are a large number of research avenues to be addressed in the future. The first objective for future research is to implement a more precise research field of business research. The second objective is to conduct more comparative studies between Twitter sentiment analysis and qualitative business research methods. Another intriguing research topic is Twitter sentiment analysis in the context of Finnish companies.Tutkielman tiivistelmätiedoissa näkyvä hyväksymisvuosi on 2019.The year of approval showing in the abstract of the thesis is 2019

Analyzing user reviews of messaging Apps for competitive analysis

Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Data ScienceThe rise of various messaging apps has resulted in intensively fierce competition, and the era of Web 2.0 enables business managers to gain competitive intelligence from user-generated content (UGC). Text-mining UGC for competitive intelligence has been drawing great interest of researchers. However, relevant studies mostly focus on industries such as hospitality and products, and few studies applied such techniques to effectively perform competitive analysis for messaging apps. Here, we conducted a competitive analysis based on topic modeling and sentiment analysis by text-mining 27,479 user reviews of four iOS messaging apps, namely Messenger, WhatsApp, Signal and Telegram. The results show that the performance of topic modeling and sentiment analysis is encouraging, and that a combination of the extracted app aspect-based topics and the adjusted sentiment scores can effectively reveal meaningful competitive insights into user concerns, competitive strengths and weaknesses as well as changes of user sentiments over time. We anticipate that this study will not only advance the existing literature on competitive analysis using text mining techniques for messaging apps but also help existing players and new entrants in the market to sharpen their competitive edge by better understanding their user needs and the industry trends

Identifying Competitive Attributes Based on an Ensemble of Explainable Artificial Intelligence

Competitor analysis is a fundamental requirement in both strategic and operational management, and the competitive attributes of reviewer comments are a crucial determinant of competitor analysis approaches. Most studies have focused on identifying competitors or detecting comparative sentences, not competitive attributes. Thus, the authors propose a method based on explainable artificial intelligence (XAI) that can detect competitive attributes from consumers’ perspectives. They construct a model to classify the reviewer comments for each competitive product and calculate the importance of each keyword in the reviewer comments during the classification process. This is based on the assumption that keywords significantly influence product classification. The authors also propose an additional novel methodology that combines various XAI techniques such as local interpretable model-agnostic explanations, Shapley additive explanations, logistic regression, gradient-based class activation map, and layer-wise relevance propagation to build a robust model for calculating the importance of competitive attributes for various data sources

Opinion mining and sentiment analysis in marketing communications: a science mapping analysis in Web of Science (1998–2018)

Opinion mining and sentiment analysis has become ubiquitous in our society, with applications in online searching, computer vision, image understanding, artificial intelligence and marketing communications (MarCom). Within this context, opinion mining and sentiment analysis in marketing communications (OMSAMC) has a strong role in the development of the field by allowing us to understand whether people are satisfied or dissatisfied with our service or product in order to subsequently analyze the strengths and weaknesses of those consumer experiences. To the best of our knowledge, there is no science mapping analysis covering the research about opinion mining and sentiment analysis in the MarCom ecosystem. In this study, we perform a science mapping analysis on the OMSAMC research, in order to provide an overview of the scientific work during the last two decades in this interdisciplinary area and to show trends that could be the basis for future developments in the field. This study was carried out using VOSviewer, CitNetExplorer and InCites based on results from Web of Science (WoS). The results of this analysis show the evolution of the field, by highlighting the most notable authors, institutions, keywords, publications, countries, categories and journals.The research was funded by Programa Operativo FEDER Andalucía 2014‐2020, grant number “La reputación de las organizaciones en una sociedad digital. Elaboración de una Plataforma Inteligente para la Localización, Identificación y Clasificación de Influenciadores en los Medios Sociales Digitales (UMA18‐ FEDERJA‐148)” and The APC was funded by the same research gran

Context Based Classification of Reviews Using Association Rule Mining, Fuzzy Logics and Ontology

The Internet has facilitated the growth of recommendation system owing to the ease of sharing customer experiences online. It is a challenging task to summarize and streamline the online textual reviews. In this paper, we propose a new framework called Fuzzy based contextual recommendation system. For classification of customer reviews we extract the information from the reviews based on the context given by users. We use text mining techniques to tag the review and extract context. Then we find out the relationship between the contexts from the ontological database. We incorporate fuzzy based semantic analyzer to find the relationship between the review and the context when they are not found therein. The sentence based classification predicts the relevant reviews, whereas the fuzzy based context method predicts the relevant instances among the relevant reviews. Textual analysis is carried out with the combination of association rules and ontology mining. The relationship between review and their context is compared using the semantic analyzer which is based on the fuzzy rules

Social media competitive analysis and text mining: a case study in digital marketing in the hospitality industry

Objectives The main objectives of this study were to explore the effectiveness of using text mining to analyse the consumer generated content from online hotel reviews. Specifically, this study focuses on demonstrating the helpfulness of such tools in the case of Original Sokos Hotel Vaakuna Helsinki and Scandic Marski in Finland. By analyzing the current trends and patterns of the online reviews of the two hotels, the objective of the study is to understand the extent to which text mining can improve marketing decisions and thus bring value to consumers. Summary The tourism and hospitality industry has changed tremendously due to the emergence of online review platforms such as TripAdvisor.com. This study applies text mining analytics to conduct a content analysis on the social media content provided by hotel guests on these platforms. To gain competitive insights from the data, topic classification and sentiment analysis are used. Conclusions The findings of the research illustrate how topics and related sentiment can be identified from the online content. Although there are several similarities between the data regarding online discussion, the text mining analysis also identified some differences, which have the potential to contribute to gaining competitive intelligence in the industry. Overall, the study illustrates how simple text mining software, which requires little resources from firms can provide beneficial information about the market to hotels in international business