<p>The primary concern discussed in this paper is the analysis of WhatsApp chat data using natural language processing (NLP). The escalating use of mobile messaging applications has led to the generation of descriptive text data. This data contains information that can be helpful to understand the dynamics of communication, user behavior, and his/her sentments and emotions. All these valuable pieces of information can be gathered from the data by using NLP and sentiment analysis (SA) techniques. In achieving this objective, the paper focuses on performing emotion analysis, depression detection, and sentiment analysis to gain insights in the patterns of communication and emotional behavior. While SA indicates the general attitude in conversations, depression detection shows the signs of being potentially depressed. This work aims to advance the field of WhatsApp chat analysis by developing a consistent and intuitive framework for performing sentiment analysis, depression detection, and emotion analysis. The techniques improve the gaps left by contemporaries in languages other than English, thus giving more value to the users who wish to analyze communication patterns, emotional health, and activities performed over time using the comprehensive standard offered by the proposed framework. It tackles the issue of Hinglish, which is a blend of Hindi and English, and utilizes Python libraries like NLTK and TextBlob, which handles NLP algorithms and visualizations. Results are captured and shared in a sleek web application built using Streamlit which allows for intuitive access to the insights obtained from sentiment, depression, and emotion analysis performed. It is believed that the paper will add to knowledge about the emotional interactions and wellness of the chat community.</p>