Online Text Mining Research Articles

How does travel satisfaction affect preference for shared electric vehicles? An empirical study using large-scale monitoring data and online text mining

Shared electric vehicles (SEVs) are an emerging mode of transportation that offers advantages in environmental protection. It is unclear what role travel satisfaction plays in operations. User's feedback is an important factor to the future development of SEVs. How to accurately collect user's feedback and predictions is the focus of attention. In this paper, we innovatively used sentiment analysis to construct the travel satisfaction index based on text data from the largest social network platforms in China. In addition, we used a vector autoregression model to analyse and confirm that users' trip satisfaction is an important factor influencing subsequent use of SEVs. A large-scale data set of travel records combined with point of interest information of SEVs that covered 1.64 million records of 3,100 vehicles. The results showed that low rates of satisfaction of SEVs are attributed to the fault rate of vehicles and poor services. Arranging SEV services around restaurants and commercial areas will result in higher user's satisfaction. Moreover, an increase in user's satisfaction will increase the usage frequency of SEVs in future trips. An increase in satisfaction will reduce rental times of returning users in the short term, but it has no effect on travel distance.

Investigation on the Awareness and Preference for Wood Culture to Promote the Values of Wood: III. Living Environment and Trend of Wood Utilization

Improving public awareness of wood is crucial for expanding the use of wood and establishing a wood culture. In this study, the environment and trends of the public’s wood utilization were analyzed using a questionnaire survey and online text mining at a time when eco-friendly practices are gaining popularity. As a result of reducing climatic and environmental concerns and its positive physical and psychological effects, the use of wood is predicted to rise in areas intimately connected to everyday living, such as wood furniture, wooden structures, and interior materials. Nonetheless, there was a negative awareness that wood was expensive, difficult to maintain, and associated with deforestation. The correlations between wood-related search terms on major Korean portal sites were analyzed and categorized into five groups: Wooden architecture, cultural education, woodworking, wood industry, and wood policy. As a building material, wood was seen as more traditional and friendly than reinforced concrete and stone. Eighty-six percent of respondents expected to utilize wood as a building material in the future, regardless of whether the wood is domestically produced or imported. Sixty-five percent responded favorably about the effects of wood on the health of wooden home inhabitants. It is believed that both active publicity and quantifiable value analysis of human and environmental friendliness are required to increase pro-environment awareness of wood utilization.

백화점 체험 경향에 관한 대중적 인식 텍스트마이닝 연구

Department stores are emphasizing customer experience in the online era as an offline store. Therefore, this paper aims to reveal the trend of experience in department store through online text mining, a big data research. Literature review was conducted on the development of department stores, experience marketing and the department stores as cultural providers. Textmining was followed with keywords ‘department store’ and ‘experience’ to know the public perception of the overall department store experience as a pilot study. Based on the frequency analysis of pilot study, cases were selected for more detailed analysis. In addition, a framework for analysis was prepared using the 4Ps, 4Es and 3 steps of experiential marketing. Then, Text mining was conducted using centrality analysis on selected cases. The result is as follows. Firstly, the public has a high awareness of the department store experience, and the department store experience is diversifying. The categories can be summarized as food and beverage experience, store experience, nature experience, children’s experience, photography/play experience, exhibition experience, art experience, and cultural experience. Secondly, the elements of experience showed high levels of aesthetic elements and entertainment elements indicating passive participation based on store experiece and F&B Experience. The escapist elements that require active participation for a holistic experience were also experienced trough exhibition experience, providing strong immersion using media and the latest technology, utilizing the five senses of viewers. Thirdly, the experience of a department store is perceived as holistic experience, indicating that experiential marketing is an important part of brand communication. Therefore, In planning the experience, the steps before the experience, participation in the experience, and the stage after the experience should be considered at the same time. Lastly, target customers can be broadly divided into young couples, children, young lovers, and friends.

How Does Travel Satisfaction Affect Preference for Shared Electric Vehicles? An Empirical Study Using Large - Scale Monitoring Data and Online Text Mining

How Does Travel Satisfaction Affect Preference for Shared Electric Vehicles? An Empirical Study Using Large - Scale Monitoring Data and Online Text Mining

Customer Perception towards Networked Streaming Service Providers with Reference to Amazon Prime and Netflix

Online video streaming services like Amazon Prime and Netflix have redefined the concept of movie watching and entertainment. They have become hugely popular worldwide. Their subscribers are increasing day by day. There is also intense competition between the two for getting viewer attention. In this context, the study aims at understanding the viewer perception about the two online service providers through viewer opinions collected from different social media platforms like Instagram, Facebook, Twitter, blogs, discussion forums, etc. Viewer opinions expressed online becomes electronic word-of-mouth for potential viewers who often assess the two platforms using the reviews of their peers. The opinions were checked for sentiments relating to content, audio/video quality, ease of use, and price related aspects of Netflix and Amazon Prime using the lens of e-service quality. The study analyses how sentiment analysis in Python has brought out the most favored service provider in terms of positive viewer reviews. For the study, the primary data was collected from reviews and was analyzed to look for patterns using the online text mining software called Voyant tool. The result of study states that Ease of use is an important aspect of e-service quality since both Amazon and Netflix are offered through technology aided platforms

Analysis and Implications of Travel Experience and Perception in Border Ethnic Areas Based on Online Text Mining——A Case Study of Xishuangbanna in Yunnan Province

The virtual online community has become an important platform for tourists to share their experience and perception of the tourist spots. Due to the originality without artistic processing, the content generated by internet users makes the information relatively more authentic and trustworthy. By taking Xishuangbanna as a typical area of border ethnic areas for case studies, the paper crawls tourists comments from Ctrip.com, through text mining, and quantitatively analyzes the high-frequency words regarding tourists’ experience and their perception in Xishuangbanna, covering such aspects as semantics, time series, value dimension, multi-dimensional scale, in order to understand the travel experience demand and market development trend in border ethnic areas. It is found that tourists have a relatively good perception of their travel experience in Xishuangbanna, and the overall evaluation of travel experience and perception is mainly positive; the product perception diversity is obvious; there are significant differences in various types of tourism elements perception, and tourists have formed clear and typical perception groups in terms of their travel experience in Xishuangbanna. Accordingly, this paper suggests that three ‘trump cards’ should be played for the future development of tourism in Xishuangbanna. Specifically, consumers for night tourism should be nurtured. The tourism elements system should be optimized, and the quality of tourism service facilities be improved. Meanwhile, market supervision should be strengthened and the level of travelling convenience be enhanced so as to optimize the tourists’ experience and perception of border tourism.

SLANGZY: a fuzzy logic-based algorithm for English slang meaning selection

The text present on online forums and social media platforms conventionally does not follow a standard sentence structure and uses words that are commonly termed as slang or Internet language. Online text mining involves a surfeit of slang words; however, there is a distinct lack of reliable resources available to find accurate meanings of these words. We aim to bridge this gap by introducing SLANGZY, a fuzzy logic-based algorithm for English slang meaning selection which uses a mathematical factor termed as “slang factor” to judge the accuracy of slang word definitions found in Urban Dictionary, the largest Slang Dictionary on the Internet. This slang factor is used to rank definitions of English slang words retrieved from over 4 million unique words on popular social media platforms such as Twitter, YouTube and Reddit. We investigate the usefulness of SLANGZY over Urban Dictionary to find meanings of slang words in social media text and achieve encouraging results due to recognizing the importance of multiple criteria in the calculation of slang factor in the algorithm over successive experiments. The performance of SLANGZY with optimum weights for each criterion is further assessed using the accuracy, error rate, F-Score as well as a difference factor for English slang word definitions. To further illustrate the results, a web portal is created to display the contents of the Slang Dictionary consisting of definitions ranked according to the calculated slang factors.

Argo as a platform for integrating distinct biodiversity analytics tools into workflows for building graph databases

Together with the increasingly growing amount of available data on biodiversity comes the proliferation of various informatics tools aimed at the collection, management and analysis of biodiversity-relevant knowledge. Consequently, we have seen how several data formats and programming languages or environments have come into use, giving rise to a problem in interoperability should anyone wish to combine the outputs of distinct tools, or to integrate them into one solution. Argo (Rak et al. 2012), an online text mining workbench based on the Unstructured Information Management Architecture (UIMA) interoperability standard, offers a means for seamlessly unifying various tools and resources into customisable text processing workflows. Among many other features, Argo provides: (1) a library of diverse tools, i.e., UIMA components, each of which is dedicated to a specific task such as loading datasets or gazetteers of interest (e.g., the Biodiversity Term Inventory), recognition of species names and their semantically related terms (Nguyen et al. 2017); (2) a graphical interface for designing workflows using components as building blocks; (3) an environment for executing and monitoring the progress of workflows; and (4) a user-interactive annotation editor for manually revising or validating results of automated processing. Recently, Argo has been extended to provide support for incorporating into workflows external web services conforming with the Representational State Transfer (REST) protocol. Taking advantage of these features, we demonstrate how we combine in-house tools and resources for named entity recognition (Batista-Navarro et al. 2017) with externally developed ones, e.g., EXTRACT (Pafilis et al. 2016), in order to build text mining workflows for populating neo4j graph databases with biodiversity-relevant knowledge. To provide a few exemplars, we focus on use cases that seek to leverage various sources of literature to capture fine-grained information on the habitat and reproductive conditions of: (1) a subset of plants catalogued in World Flora Online (Jackson and Miller 2015), and (2) tropical trees belonging to the Dipterocarpaceae family.

Prediction of Online Lectures Popularity: A Text Mining Approach

Text mining is an emerging area of research with flavors of opinion mining, sentiment mining, document classification, content mining etc. Another flavor of text mining is text clustering. Proposed work is based on clustering the comments posted by users to online learning. The dataset is prepared using comments posted by users for text mining video lectures using R and Weka. In the proposed work learners comments for online text mining lectures have been clustered to observe the popularity of the lectures by analyzing the terms in each cluster.

Online Text Mining System based on M2VSM

Online Text Mining System based on M2VSM