This work offers an integrated multitool approach that relies on state-of-the-art NLP methods for real-time text analysis, specifically in sentiment analysis, personality profiling, and knowledge graph construction. The pipeline uses abstractive summarization skills from PEGASUS model to condense long inputs from the users. That is followed by a sentiment analysis process that applies BERTs to classify the summarized text’s emotional sentiment as either positive, negative, or neutral. The framework also derives personality traits from emotion and expects probable future behaviors by mapping the sentiment graph against the model defining the traits. Besides, for text preprocessing, we use the NLTK library for tokenization and removing stopwords, always extracting important keywords from users’ inputs. These keywords are then used to build a knowledge graph, which is then implemented using NetworkX and Matplotlib to show connections between the identified ideas. This knowledge graph is used for generating the forecast of interconnections between the keywords to provide a clear and concise approach in comparison with the complex interconnection maps. The proposed system enables input text to be in a different language and the output summaries, sentiments, and knowledge graphs in the same required language as the input text. Combinedly, the framework intends to provide real-time, precise analysis of the contents of social media posts for future course of action prediction and for use in future applications like health, mental health checks, and analysis of social behavior.