The area of digital analytics is explored in the current article. Social media analytics is an important process that involves gathering and analyzing publicly discussed topics and mentions from a range of social media and online platforms. Natural language processing is used in real time on an automated software system that allows a user to ask questions in natural language and receive the response they want. A subfield of artificial intelligence that primarily focuses on human–computer communication is natural language processing. Text and speech processing, anatomical inquiry, syntactic exploration, lexical semantics, comparative semantics, etc., are typical NLP activities. A software system with an effective natural language processing (NLP) approach is required to process user inquiries, given the variety of natural language query data that is handled daily. Regarding natural language processing, there are several benefits and drawbacks. The main benefit is how easy it is to process the user-friendly query and get the desired outcome. The hindrance would be that whenever handling native language interrogation, it would be necessary to convert the corresponding language interrogation into one that is machine-understandable and then process the interrogation-related data. In contrast, the user would not need to be aware of the language changeover or the query processing strategy. The majority of businesses struggle with the challenge of maintaining enormous dataset collections. These businesses frequently consist of several divisions that operate separately and create data silos—unlinked collections of semantically related data. There is possibly inconsistency and redundancy in this material. Integrating the data silos is crucial to making effective use of this data. It takes time and money to integrate data silos, scale them to meet present needs, and grant departments the right access. As a result, a lot of business analysts integrate data haphazardly, which is ineffective, time-consuming, frequently leads to issues with data quality, and has a detrimental effect on business decision-making. Large amounts of social media data are gathered, processed, and analyzed by the proposed system’s frontend, master server, database and crawlers, and analytics modules. In addition to offering descriptive statistics, hashtag analysis, sentiment analysis, text summarization, and subject recommendations, the analytics module also uses Named Entity Recognition (NER) and sentiment analysis to identify influential individuals and respond to consumer inquiries. The difficulties in preprocessing social media data, such as cleaning, spam filtering, and language normalization, are highlighted in the article. It also investigates different models for spam detection and sentiment analysis, with the Linear Support Vector Classifier (SVC) demonstrating the best results in spam detection. The use of graph-based techniques for summarizing social media postings, the study also covers text summary techniques, such as extractive and abstractive summarization. The suggested method seeks to enhance company decision-making, lower expenses, and provide insightful information on public attitude and trends by automating the analytical process. Future development will concentrate on improving performance, increasing scalability, and expanding the system’s support for a variety of data formats. The results highlight the value of automation in social media monitoring, which helps companies remain competitive by extracting useful information from user-generated material.