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WhatsApp is a widely popular messaging app used for communication. This study focuses on analyzing WhatsApp group chats, aiming to assess member activity and engagement. It seeks to determine the most active day, the highest number of messages sent on that day, the most active user, active group members, the total user count, individual message contributions, and the most frequently used phrase. Additionally, it conducts daily, monthly, and annual analyses of the top 15 members’ messages. Furthermore, the study aims to predict emotions within the text, such as joy, sadness, and anger, using a deep learning algorithm known as LSTM. The model's performance is evaluated using a dataset from Kaggle comprising 16,000 English language records with various emotions, with validation accuracy as the key metric for emotion analysis.

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WENSA: WhatsApp Emotion and Sentiment Analyzer Using LSTM and BI-LSTM

  • 会议论文

  • 发表日期：

    2025年

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  • N. A. Abhinav Kumar,
  • R. V. Sanjay,
  • V. Vani,
  • N. Karthik

摘要

WhatsApp is a widely popular messaging app used for communication. This study focuses on analyzing WhatsApp group chats, aiming to assess member activity and engagement. It seeks to determine the most active day, the highest number of messages sent on that day, the most active user, active group members, the total user count, individual message contributions, and the most frequently used phrase. Additionally, it conducts daily, monthly, and annual analyses of the top 15 members’ messages. Furthermore, the study aims to predict emotions within the text, such as joy, sadness, and anger, using a deep learning algorithm known as LSTM. The model's performance is evaluated using a dataset from Kaggle comprising 16,000 English language records with various emotions, with validation accuracy as the key metric for emotion analysis.