A Survey on Classification Techniques for Feature-Sentiment Analysis

As use of internet and its application are growing exponentially; the e-commerce business i.e. online purchase is proportionately swelling in the world. The e-commerce websites and similar service providing websites are providing a rich variety of product and service to be sold. As the quality of service and product/goods has much effect on its sell, the websites nowadays tends to have public opinion on the product in the form of feedback; we can name it as reviews. These reviews provide much information about the service/product as the customers are encouraged to write their reviews cum assessments about the product, more precisely saying, customer writes their view about product’s specifications or product’s features. These unrestricted or restricted opinions from public can then be considered by the customers and vendor to make the required design/engineering/production changes to the product to upsurge its quality. The Feature Mining along with Sentiment Analysis techniques can be applied to achieve product’s feature and public opinion on these features. Here in this paper we are interestingly motivated by the scenario as discussed above. We had a survey on the different methods cum techniques that can be usually used to extract products/service features and categorizing those feature along with the sentiment classification on the determined features which is part of Machine learning. The public opinions can be classified as positive, negative and neutral sentimentalities. Research area ‘Data Mining’ has proven its importance with its rich set of Machine Learning Algorithms which in turn can be used as Sentiment or Opinion Classifier. After evaluating feature-sentiment techniques, we then studied the feature classification/categorizing by using its overall sentiment and influence on the product/service sell. DOI: 10.17762/ijritcc2321-8169.15079

Uso de text mining na determinação da relação entre instituições de investigação e empresas

A excelência da investigação científica produzida é indissociável da inovação e criação de valor económico e social. Assim, a aproximação entre diferentes áreas científicas e de negócio, e a consolidação de formas de colaboração entre as instituições de investigação e o tecido económico e social, potencia a criação, transferência e valorização do conhecimento. Neste processo têm particular importância as unidades de investigação e as empresas, com papeis diferenciados, mas essenciais para completar o ciclo de inovação. No mundo competitivo de hoje, a capacidade de extrair conhecimento útil de dados e tomar decisões de acordo com esse conhecimento é cada vez mais importante e essencial. O processo de aplicação de metodologias para extração de conhecimento a partir de dados textuais é denominado de text mining. A sua utilização tem como benefício a grande quantidade de informação importante latente neste formato e que não está disponível nos formatos clássicos de dados estruturados. Neste trabalho foi desenvolvida uma ferramenta de apoio estratégico, que tem como objetivo potenciar a colaboração entre unidades de investigação e o tecido empresarial. Através de metodologias de text mining, nomeadamente sumário da informação e topic modeling com utilização do modelo Latent Dirichlet Allocation, é analisada a informação disponível nos websites das unidades de investigação e das empresas e são identificadas possíveis relações. É a primeira vez que é analisado este tipo de informação com recurso a metodologias de text mining e com o propósito de potenciar a relação entre estas estruturas. A análise de resultados obtidos permitiu concluir que os mesmos estão dependentes da qualidade da informação disponível nos websites e da representatividade de todas as áreas. Caso estas condições sejam garantidas, esperam-se bons resultados relativos a possíveis relações, tendo sempre em consideração que estes resultados poderão não ser os mais óbvios tendo por base o conhecimento prévio das entidades em análise. Assim, a ferramenta deverá ser utilizada como apoio na tomada de decisão, devendo os resultados obtidos ser analisados de forma crítica e em complemento à experiência de especialistas.The excellence of the scientific research produced is inseparable from innovation and the creation of economic and social value. Thus, the approximation between different scientific and business areas and the consolidation of forms of collaboration between research institutions and the economic and social fabric, boosts the creation, transfer and valorization of knowledge. In this process, research units and companies are particularly important, with different but essential roles to complete the innovation cycle. In today’s competitive world, the ability to extract useful knowledge from data and make decisions according to that knowledge is increasingly important and essential. The process of applying methodologies to extract knowledge from textual data is called text mining. Its use has the benefit of accessing to a large amount of important information latent in this format and which is not available in the classic structured data formats. In this work a strategic support tool was developed, aiming to enhance collaboration between research units and the business fabric. Through the use of text mining methodologies, namely information summary and topic modeling using the Latent Dirichlet Allocation model, the information available on the websites of research units and companies is analyzed and possible connections are identified. It is the first time that this type of information is analyzed using text mining methodologies and with the purpose of enhancing the relationship between these structures. The analysis of results led to the conclusion that these results depend on the quality of the information available on the websites and the representativeness of all areas. If these conditions are guaranteed, good results are expected regarding possible connections, always bearing in mind that these results may not be the most obvious, based on the prior knowledge of the entities under analysis. Therefore, the tool should be used as a support in decision making and the results obtained should be critically analyzed in addition to the experience of experts

Using webcrawling of publicly available websites to assess E-commerce relationships

We investigate e-commerce success factors concerning their impact on the success of commerce transactions between businesses companies. In scientific literature, many e-commerce success factors are introduced. Most of them are focused on companies' website quality. They are evaluated concerning companies' success in the business-to- consumer (B2C) environment where consumers choose their preferred e-commerce websites based on these success factors e.g. website content quality, website interaction, and website customization. In contrast to previous work, this research focuses on the usage of existing e-commerce success factors for predicting successfulness of business-to-business (B2B) ecommerce. The introduced methodology is based on the identification of semantic textual patterns representing success factors from the websites of B2B companies. The successfulness of the identified success factors in B2B ecommerce is evaluated by regression modeling. As a result, it is shown that some B2C e-commerce success factors also enable the predicting of B2B e-commerce success while others do not. This contributes to the existing literature concerning ecommerce success factors. Further, these findings are valuable for B2B e-commerce websites creation

Information Technology Applications in Hospitality and Tourism: A Review of Publications from 2005 to 2007

The tourism and hospitality industries have widely adopted information technology (IT) to reduce costs, enhance operational efficiency, and most importantly to improve service quality and customer experience. This article offers a comprehensive review of articles that were published in 57 tourism and hospitality research journals from 2005 to 2007. Grouping the findings into the categories of consumers, technologies, and suppliers, the article sheds light on the evolution of IT applications in the tourism and hospitality industries. The article demonstrates that IT is increasingly becoming critical for the competitive operations of the tourism and hospitality organizations as well as for managing the distribution and marketing of organizations on a global scale

Prediction Techniques in Internet of Things (IoT) Environment: A Comparative Study

Socialization and Personalization in Internet of Things (IOT) environment are the current trends in computing research. Most of the research work stresses the importance of predicting the service & providing socialized and personalized services. This paper presents a survey report on different techniques used for predicting user intention in wide variety of IOT based applications like smart mobile, smart television, web mining, weather forecasting, health-care/medical, robotics, road-traffic, educational data mining, natural calamities, retail banking, e-commerce, wireless networks & social networking. As per the survey made the prediction techniques are used for: predicting the application that can be accessed by the mobile user, predicting the next page to be accessed by web user, predicting the users favorite TV program, predicting user navigational patterns and usage needs on websites & also to extract the users browsing behavior, predicting future climate conditions, predicting whether a patient is suffering from a disease, predicting user intention to make implicit and human-like interactions possible by accepting implicit commands, predicting the amount of traffic occurring at a particular location, predicting student performance in schools & colleges, predicting & estimating the frequency of natural calamities occurrences like floods, earthquakes over a long period of time & also to take precautionary measures, predicting & detecting false user trying to make transaction in the name of genuine user, predicting the actions performed by the user to improve the business, predicting & detecting the intruder acting in the network, predicting the mood transition information of the user by using context history, etc. This paper also discusses different techniques like Decision Tree algorithm, Artificial Intelligence and Data Mining based Machine learning techniques, Content and Collaborative based Recommender algorithms used for prediction

Weak signal identification with semantic web mining

We investigate an automated identification of weak signals according to Ansoff to improve strategic planning and technological forecasting. Literature shows that weak signals can be found in the organization's environment and that they appear in different contexts. We use internet information to represent organization's environment and we select these websites that are related to a given hypothesis. In contrast to related research, a methodology is provided that uses latent semantic indexing (LSI) for the identification of weak signals. This improves existing knowledge based approaches because LSI considers the aspects of meaning and thus, it is able to identify similar textual patterns in different contexts. A new weak signal maximization approach is introduced that replaces the commonly used prediction modeling approach in LSI. It enables to calculate the largest number of relevant weak signals represented by singular value decomposition (SVD) dimensions. A case study identifies and analyses weak signals to predict trends in the field of on-site medical oxygen production. This supports the planning of research and development (R&D) for a medical oxygen supplier. As a result, it is shown that the proposed methodology enables organizations to identify weak signals from the internet for a given hypothesis. This helps strategic planners to react ahead of time