According to the National Security Agency, the Internet processes 1826 petabytes (PB) of data per day [1]. In 2018, the amount of data produced every day was 2.5 quintillion bytes [2]. Previously, the International Data Corporation (IDC) estimated that the amount of generated data will double every 2 years [3], however 90% of all data in the world was generated over the last 2 years, and moreover Google now processes more than 40,000 searches every second or 3.5 billion searches per day [2]. Facebook users upload 300 million photos, 510,000 comments, and 293,000 status updates per day [2, 4]. Needless to say, the amount of data generated on a daily basis is staggering. As a result, techniques are required to analyze and understand this massive amount of data, as it is a great source from which to derive useful information.

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Alzheimer's disease is a progressive illness that affects more than 5.5 million people in the United States with no effective cure or treatment. Symptoms of the disease include declines in memory and speech abilities and increases in aggression and insomnia. Recent research suggests that NLP techniques can detect early cognitive decline as well as monitor the rate of decline over time. The processed data can be used in a smart home environment to enhance the level of home care for Alzheimer's patients. This paper proposes early-stage research in software engineering and natural language processing for quantifying and evaluating the patient's cognitive state to determine the required level of support in a smart home.