Application of NLP-based topic modeling to analyse unstructured text data in annual reports of construction contracting companies

Abstract

The construction industry is the backbone of a nation’s economy. It is a matter of great concern that such an industry suffers from time and cost overruns, especially in these challenging times. Coupled with the overrun issues, the sector is often criticized for lacking adequate quality and quantity of structured secondary data. The emerging technologies in data science and machine intelligence present a unique opportunity to understand the sector better and aid in effective decision-making. To better understand the utility of such technologies, the Management Discussion and Analysis ssections of the annual reports of publicly listed top Indian construction contracting firms are analyzed to identify the presence of ‘strategy themes’ and further map them to the organizations considered. Natural Language Processing (NLP)-based topic modeling algorithms, namely Latent Dirichlet Allocation (LDA) and Non-negative Matrix Factorization (NMF), are used in this study to perform a qualitative content analysis to identify the latent themes. From a methodological standpoint, considering the context of this study, the NMF results are better in accuracy, precision, and recall compared with the LDA. The results show that while most construction contracting firms prioritized a ‘revenue-focused’ strategy to expand their order books, a smaller set of large-sized firms seem to prioritize process improvement to improve their execution productivity and therefore are ‘profit margin improvement focused’ or ‘lean-focussed’ in their approach. Although a proof-of-concept, this study unlocks the immense potential of unsupervised NLP-based topic-modeling tools to understand and infer from unstructured and freely available text data in the public domain to aid sectoral analysis and policymaking.

Unveiling the Evolution of Enterprise Digital Innovation Strategies: Insights From U.S.-Listed Companies’ Annual Reports

“…The texts published by firms contain mainly corporate annual reports, earning call transcripts, CSR reports, corporate environmental reports, analyst reports, etc. Jagannathan and Niveditha's study used topic model to extract strategic topics and CSR-related content from corporate annual reports, individually [31], [32]. Huang's research used topic modeling for corporate earning call transcripts from which information of interest to investors was extracted, in turn, to explore the role analysts play in investment behavior [33].…”

Artificial Intelligence in Knowledge Management for Construction-Specific Documents

“…Humans are finding it more difficult to digest large amounts of information contained in text documents that are presented in an unstructured textual style. This issue emphasizes the significance of automatic reading and natural language comprehension [26]. It may be necessary to prevent manual reading of texts inside the initial corpus, depending on the domain.…”

Analisis Dan Penerapan Topic Modeling Pada Judul Tugas Akhir Mahasiswa Menggunakan Metode Latent Dirichlet Allocation (Lda)

“…Topic modeling adalah sebuah teknik untuk menemukan topik atau tema utama dari sejumlah besar dokumen tidak terstruktur yang berbentuk teks yang dapat digunakan untuk membuat suatu kesimpulan, termasuk topik tersembunyi yang menghasilkan kumpulan frasa dan kata yang menurut mereka terkait [21] [23]. Topik berisi sekelompok kata-kata yang sering muncul bersamaan, dan pemodelan topik dapat menghubungkan kata-kata yang memiliki arti serupa dan dapat membedakan antara penggunaan kata dengan banyak makna (Alghamdi & Alfalqi, 2015).Sederhananya pemodelan topik adalah teknik penambangan teks untuk menemukan topik (Blei, 2012).…”