Exploratory Data Analysis

Cox, Victoria

doi:10.1007/978-1-4842-2256-0_3

Cited by 33 publications

(19 citation statements)

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“…The study followed exploratory qualitative data analysis techniques for data analysis. Exploratory qualitative data analysis allows for the amalgamation of data from several sources to draw generalizations and generate understanding from shared realities, experiences and opinions (Cox, 2017; Young, 1981). After manually transcribing the interviews, the transcribed data were coded and analyzed according to concepts and indicators of clearly defined issues.…”

Section: Methodsmentioning

confidence: 99%

Shaping organizational culture in response to tourism seasonality: A qualitative approach

Senbeto

Hon

2021

Journal of Vacation Marketing

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This study focuses on understanding response to seasonal variation in tourism from organizational cultures and values context. Based on in-depth semi-structured interviews with hotel managers in Ethiopia, we argued that organizational cultures and regulatory processes influence hotels’ response to seasonal variation. The study provides a framework to understand the response to tourism seasonality. In addition, the findings highlight SDG 2030 (Sustainable Development Goals) to ensure productive tourism in the developing economies by mitigating seasonal variation. Implications and directions for future research are discussed.

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Section: Methodsmentioning

confidence: 99%

Shaping organizational culture in response to tourism seasonality: A qualitative approach

Senbeto

Hon

2021

Journal of Vacation Marketing

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“…The next step is EDA which is usually carried out to understand the data better, look for anomalous data, see important variables and also get initial insights. EDA is usually carried out either statistically or using graphs (data visualization) (Cox, 2017;Guzman et al, 2017;Halibas et al, 2019;Wahyuni et al, 2019). EDA carried out in this study will also use statistics and graphics.…”

Section: Methodsmentioning

confidence: 99%

Feature Extraction for Sentiment Analysis in Indonesian Twitter

Wahyuni

Arifiyanti

Afandi

2021

Nusantara Science and Technology Proceedings

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Twitter's sentiment analysis is one of the most interesting fields of research lately. It intertwines the natural language processing techniques with data mining. Up to this point, many algorithms have been proposed to better understand sentiment from text. The proposed method can be focused on the preprocessing step, dataset splitting method (training and testing), dataset balancing method (when the data is unbalanced), to the improvement of the existing algorithm. But, the main focus of this paper is on feature extraction from tweets using TF-IDF. The features obtained from this process are expected to improve the accuracy of the classification process. The dataset used in this research is in Indonesian, which has a very different form when compared to English. This dataset consists of 1068 manually labeled tweets related to the "school from home" policy caused by the COVID-19 outbreak, taken from March to July. All steps required to process this data will be implemented using python. To validate its utility, the performance of the proposed method is compared with each other. Finally, the results are summarized by reflecting on the impact of the inclusion of the proposed features for each classification algorithm for sentiment detection

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“…Furthermore, it can show us hidden relationships and attributes present in our data before using it for text mining modelling. [50,51]…”

Section: Exploratory Data Analysis (Eda)mentioning

confidence: 99%

Natural Language Processing Methods for Scoring Sustainability Reports – A Study of Nordic Listed Companies

Gutierrez-Bustamante¹,

Espinosa-Leal²

2022

Preprint

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This paper investigates if Corporate Social Responsibility (CSR) reports published by a selected group of Nordic companies are aligned with the Global Reporting Initiative (GRI) standards. To achieve this goal, several natural language processing, and text mining techniques were implemented and tested. We extracted strings, corpus, and hybrid semantic similarities from the reports and evaluated the models through the intrinsic assessment methodology. A quantitative ranking score based on index matching was developed to complement the semantic valuation. The final results show that Latent Semantic Analysis (LSA) and Global Vectors for Word Representation (GloVE) are the best methods for our study. Our findings will open the door to the automatic evaluation of sustainability reports which could have a strong impact on the environment.

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Exploratory Data Analysis

Cited by 33 publications

References 0 publications

Shaping organizational culture in response to tourism seasonality: A qualitative approach

Shaping organizational culture in response to tourism seasonality: A qualitative approach

Feature Extraction for Sentiment Analysis in Indonesian Twitter

Natural Language Processing Methods for Scoring Sustainability Reports – A Study of Nordic Listed Companies

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