Business environmental analysis for textual data using data mining and sentence-level classification

Abstract

Purpose The purpose of this paper is to propose a methodology to analyze a large amount of unstructured textual data into categories of business environmental analysis frameworks. Design/methodology/approach This paper uses machine learning to classify a vast amount of unstructured textual data by category of business environmental analysis framework. Generally, it is difficult to produce high quality and massive training data for machine-learning-based system in terms of cost. Semi-supervised learning techniques are used to improve the classification performance. Additionally, the lack of feature problem that traditional classification systems have suffered is resolved by applying semantic features by utilizing word embedding, a new technique in text mining. Findings The proposed methodology can be used for various business environmental analyses and the system is fully automated in both the training and classifying phases. Semi-supervised learning can solve the problems with insufficient training data. The proposed semantic features can be helpful for improving traditional classification systems. Research limitations/implications This paper focuses on classifying sentences that contain the information of business environmental analysis in large amount of documents. However, the proposed methodology has a limitation on the advanced analyses which can directly help managers establish strategies, since it does not summarize the environmental variables that are implied in the classified sentences. Using the advanced summarization and recommendation techniques could extract the environmental variables among the sentences, and they can assist managers to establish effective strategies. Originality/value The feature selection technique developed in this paper has not been used in traditional systems for business and industry, so that the whole process can be fully automated. It also demonstrates practicality so that it can be applied to various business environmental analysis frameworks. In addition, the system is more economical than traditional systems because of semi-supervised learning, and can resolve the lack of feature problem that traditional systems suffer. This work is valuable for analyzing environmental factors and establishing strategies for companies.

Support for decision-making in checking the level of quality of student research works based on automated text analysis (Asistencia para la toma de decisiones en la evaluación de la calidad de las investigaciones de los estudiantes basada en el análisis automático de textos)

“…The method of complex analysis of research work provides for the marking of basic fragments F in them, which represent units of research work, or a set of units combined by their purpose (semantic content). The basic objects in the process of analysing research work ( D ) are words (word types) W , composed of the natural language ALN alphabets, and in some cases, an alphabet artificially created for analysis (Kim et al, 2019).…”

A deep learning model for online doctor rating prediction

“…DL‐based models have shown better performance than the bag‐of‐words approach as they can learn low‐dimensional and continuous text representations by exploiting textual features to generate recommendations (Kim et al, 2019). Other advantages include no requirement for explicit feature engineering (Weng et al, 2020) and data representation through non‐linear transformations (Kraus & Feuerriegel, 2017).…”

Application of text mining in identifying the factors of supply chain financing risk management

“…With WSI method, construct a financial sentiment dictionary which is specific to Chinese market analytics. Kim et al (2019) utilize the similar method to train sentences classifiers and generate a dictionary specific to SWOT and PEST analytics. More recently, Kim et al (2020) propose a systematic procedure of WSI method (W2V-LSA) and identify the thematic evolution in blockchain field.…”