Artificial Intelligence Enabled Content Clustering

By Author: Kate
Total Articles: 6
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The world wide web is replete with a mammoth collection of content pieces related to different enterprises and individuals. In other words, this Big Data about different entities is difficult to group together for further analysis, especially in niche domains, such as market research services, crime intelligence, intelligence analytics.

Artificial Intelligence provides the much-needed augmented support to resolve such conundrums associated with data gathering and data grouping from multiple sources.

Gensim word2vec technique – The Artificial Intelligence solution for Big Data associations

Artificial Intelligence and Python-based solution is the answer. The Gensim word2vec technique periodically streams content having the entity names and other keywords by using Cron jobs in NoSQL databases. The solution then executes some cleansing and staging steps that require limited manual intervention. It requires some tagged words or keywords that are listed by the subject experts.

The solution then builds some models by using ...
... different parameters. It executes values for a document with “m” keywords. Further, a normalized sum of the vectors is entered into a tuple. Next, all the tuples are processed to find a similarity score. The solution then uses Cosine, Euclidean, and Manhattan distance to select an option that optimally suits a requirement. Based on the best value for the score, the plotting for the content piece and other associated content is achieved. In parallel, a distance matrix is used as a reference to relate to other new content that comes in.

The business impact of AI-enabled content clustering

For a content set of one lakh, a response time of 2 to 3 seconds is required and about 3 to 4 seconds for model creation. The response time increases in proportion for each 30% increase in the content set.

The solution reduces TAT by 50% as compared to other popular methods. It offers an intuitive model by using the word2vec model and human-in-the-loop supervision till stable scores are achieved.

Simply put

Artificial Intelligence solutions are increasingly used in different business scenarios to enable straight-through processing in most automation endeavors. However, in scenarios involving image analytics and text analytics, human-in-the-loop involvement is required. The solution discussed in the above context enables business users in the market research and intelligence analytics domain to resolve many issues related to analyzing unstructured data on the worldwide web. A balanced approach involving Artificial Intelligence and human interaction enables businesses to streamline their analysis efforts related to public content in a highly dynamic environment.