• Aditya Kumar
• Mahadev Kumar
• Deeshu sharawat

Abstract-Social media networks (SMNs) generate huge amounts of data every day, which can be structured, unstructured or semi-structured. Data can range from plain text to a graphic image or sound or video. Analyzing this diverse and ever-growing data is a major challenge. This paper focuses on extracting and analyzing data from the frequently used online text application WhatsApp through the process of sentimental and statistical analysis. Opinion mining, alsoknown as sentiment analysisis the contextual mining of data to identify, extract and analyze the underlying sentiment in news and classify it as positive, negative or neutral. Data can provide a wealth of information for cutting-edge technologies such as machine learning. Providing a machine learning model with the appropriate learning environment, which is indirectly influenced by the data it receives, is crucial. No matter what subject the chat is centred upon, the developed code can beused to provide a deeper comprehension of the facts. In order to comprehend the many analyses and feelings, this study uses the Python programming language. Keywords – Social media networks, Sentiment Analysis, Structured Emotions, Unstructured Emotions, Polarity Analysis.

"Determining the polarity and statistics of chat based on sentiment analysis", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 7, page no.f671-f681, July-2023, Available :http://www.jetir.org/papers/JETIR2307586.pdf

"Determining the polarity and statistics of chat based on sentiment analysis", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 7, page no. ppf671-f681, July-2023, Available at : http://www.jetir.org/papers/JETIR2307586.pdf