Decoding Digital Conversations: A Hybrid Sentiment Analysis Framework for WhatsApp Chat Behavioral Intelligence
Keywords:
Sentiment Analysis, Rule-Based MethodAbstract
Everyone has a curiosity about what the one person thinks about the any other person, product or services while having a conversation and judging the other person can’t be done perfectly, so this work provides a way to use sentiment analysis between conversations. When talking with someone always has a question about the picture in the other person’s mind. The first step in this process is pre- processing of the data downloaded from the WhatsApp chat which is exported to a server. After that, sentiment analysis is performed on a single message and the sentiment of all messages is normalized for overall sentiment in the way it is suggested. In this work, we conduct an in-depth analysis of WhatsApp group chat dynamics using an advanced versatility of a hybrid sentiment analysis framework that can manifest the minimum steps of preprocess- ing, lexicon-based, generative AI, and ensemble scoring analysis. This research analyzes group communication patterns using a multifaceted methodology, examining the most active days, volumes of messages, contributions of users, influences from admins, group membership, frequency of individual postings, and frequent. Combining traditional rule-based sentiment techniques with sophisticated generative AI algorithms, the study gives an independent analysis of digital group dynamics and sheds light on more intricate group behaviour patterns and usage patterns. The approach utilizes a novel ensemble scoring framework that harmonizes lexicon-based sentiment analysis with contextually- aware AI mechanisms, producing unparalleled insights into the qualitative and quantitative aspects of group communication on the web.
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