Authors - Nilesh Deotale, Nafiz Shaikh, Ashwin Katela Abstract - WhatsApp chats consist of various kinds of conversation held among two people or a group of people. This chat consists of various topics. This information can provide a lot of data for the latest technologies such as Machine Learning. The most important things for Machine Learning models are to provide the right learning experience which is indirectly affected by the data we provide to the model. This tool aims to provide in depth analysis of the data which is provided by WhatsApp. Irrespective of whichever topic the conversation is based on, our developed code can be applied to obtain a better understanding of the data. The advantage of this tool is that it is implemented using simple python modules such as pandas, matplotlib, seaborn, streamlit, NumPy, re, emojis and a technique sentiment analysis which are used to create data frames and plot different graphs, where then it is displayed in the streamlit web application which is efficient and less resources consuming algorithms, therefore it can be easily applied to larger dataset. The Accuracy of this project is 75%. In summary, this project makes use of state-of-the-art data analysis technologies such as scikit-learn, Topic Modeling, Named Entity Recognition, Clustering, Word Embeddings, Natural Language Toolkit, and more. Sequence-to-Sequence Models, Text Classification and so forth. Users can better understand how others communicate by using Language Model Fine-Tuning to extract relevant information from WhatsApp discussions.
