An introduction to natural language processing
A computer program’s capacity to recognize human language as it is verbal or on paper is known as natural language processing (NLP) – part of artificial intelligence.
NLP has been around for more than 50 years yet it has only been in more recent times that businesses have been investing in NLP as a hot property and major disruptor.
NLP has numerous practical uses in different types of industries, including market statistics, internet sites, and health research with many other applications in between.
Now, computers recognize natural language just like people through NLP. Artificial intelligence is used in natural language processing to take input from the outside world, process it, and interpret it so that a computer can perceive it.
In simple terms, computers have reading programs and microphones to collect audio, much as people have various sensors like ears to hear and eyes to see. Computers have a program to process their various inputs, just as humans have a brain to do so. The input is eventually translated into computer-readable code during processing.
The creation of algorithms and data preparation are the two fundamental stages of natural language processing. Data preparation is preparing and “cleaning” text data so that computers can analyze it.
So how does this work exactly? Different techniques are used for the preparation of data, techniques which are used named tokenization, stop word, lemmatization, etc.
After preprocessing the data algorithms are used to analyze the data. Various natural language processing algorithms exist. Different natural language processing algorithms are rule-based systems, machine learning-based systems, and so on.
Unstructured data using NLP
Different types of data cannot be identical or created equally. There are two major types of data: structured and unstructured, while structured data is simple and can be reused in a variety of ways, unstructured data is not in proper format and is much more popular and necessary.
According to International Data Corporation (IDC) by 2025, 80% of all organizational data, predicts, will be unorganized. This will present a significant issue for companies. Why? Because unstructured data analysis is not only challenging, but also time-consuming, labour-intensive, and limited in growth by manual processes.
Building automated tools to analyze unstructured data is complex since doing so may need the use of machine learning technologies like natural language processing, even though algorithms can simply analyze structured data.
Business intelligence tools can evaluate unstructured data that NLP has processed from a variety of sources, including social media, publications, and electronic medical records. Business intelligent systems through NLP can identify a business pattern and learn important insights after structuring the information. Data formats are important because they are essential to the extraction of insightful information needed to drive business choices. Businesses must assess their data strategy if they are unable to make use of both the type and volume of data to increase corporate growth and profitability.
Any organizational unstructured data management approach must begin with an understanding of the differences between structured and unstructured data, following which essential investment decisions are being made to retrieve important insights from the dataset.
Using NLP to measure corporate alignment with sustainable development goals
The Sustainable Development Goals (SDGs) provide guidance for businesses to evaluate and manage social, environmental, and financial risks while enhancing their competitive position in their industry and their market.
According to predictions, SDG-related business strategies may open up new markets. Corporations should first select the SDGs that will have the biggest impact on their business and are most relevant to them before putting any sustainable measures into place. This enables decision-makers to evaluate which areas of their business may be modified, modernized, or scaled and helps determine where the firm can have the most opportunity to contribute to Sustainable Development Goals.
Sustainability and transparency are the SDGs’ driving forces. To promote a shared discourse with other businesses, clients, and other stakeholders, businesses must be able to explain their progress and achievements and align their reports with the SDGs’ terminology.
For example, financial accounting associations jointly released disclosure guidelines that matched up frequently used reporting frameworks with how businesses are outlining their progress toward reaching SDGs. Other businesses record, monitor, and display their sustainability data utilizing cloud-based analytics platforms. Modern machine learning methods can help investors assess how closely investment connected the network to the SDGs.
Examples of Sustainable Development Goals company alignment
Using natural language processing techniques, it is possible to determine which businesses, based on the wording in their sustainability disclosures, are in line with the United Nations (UN) Sustainable Development Goals.
From 2010 to 2019, the Corporate Social Responsibility (CSR) reporting of the Russell 1000 was processed toward a logistic classifier, support vector machine (SVM), as well as a fully-connected neural network to predict alignment with the SDGs.
Both Word2Vec and Doc2Vec frameworks, in addition to the embedding’s own capabilities to support dictionary-based input data, are utilized to characterize organizations’ alignment with SDGs duration. It is noteworthy that the Doc2Vec embedding inputs to the SVM classifier produce good average accuracy for detecting alignment of above 80%.
Numerous people worldwide experience poverty, illness, racism, inequality, unemployment, and chronic illnesses. Additionally, due to factors like degradation, climate change, increasing sea levels, and loss of biodiversity, among others, they may experience natural disasters. Natural, economic, and societal issues are difficult to solve. In contrast side, the SDGs are a carefully planned and complete policy platform for improving the world [4].
In order to address the environmental, social, and economic concerns that may arise over the next 15 years, the UN adopted the SDGs in September 2015. The document’s key concepts are the five Ps (people, planet, peace, partnership, and prosperity). Unexpected effects of the COVID-19 pandemic have been felt by the SDGs. The chance of achieving the 2030 goals has decreased as a result of several SDG developments being delayed and a variety of projects being put on hold.
Conclusion
On the basis of the text of business sustainability declarations, powerful natural language processing techniques can now be used to assess how closely their operations match with the UN Sustainable Development Goals. The selection of companies that meet the social and environmental preferences of clients could be achieved through SDG assignments, for instance by choosing companies that are in line with the Sustainable Development Goals that are significant to the investor. They can be used, with a few adjustments, to gauge the degree to which existing strategies and indices are in line with particular SDGs.
