NLP is therefore the bridge that allows a symbiotic relationship between human and machine in order to offer complementing services to the improvement of content generation and decision-making. The purpose of this work is to share experiences and ideas on how the human–machine cooperation can be utilized in the creation of the NLP content and in decision-making support processes. Data acquisition for NLP applications requires the determination of the particular task and the specific audience to be served or benefited from the data to be collected, the specific data that should be collected including text documents and web data and the expertise of human beings in validating the data as well as annotating it. To increase the data quality and relevance as input to NLP applications, the following pre-processing used: tokenization pre-processing techniques—stemming/lemmatization pre-processing; and stop words removal pre-processing. Analysis methods such as sentiment analysis, topic models and named entity recognition help to reduce the time taken in NLP and guarantee that the system in question offers pertinent and precise data to the utilized. The future scope is in applying expert NLP methods for enhancing the cooperation between people and machines for more effective content creation and decision support.