Authors: Nilanjan Jana, Priyesh Pandey, Prof. Shallu Bashambu
DOI Link: https://doi.org/10.22214/ijraset.2023.53596
Certificate: View Certificate
WhatsApp, a globally used communication tool in today\'s society, is transforming into an application where individuals express their thoughts, feelings, and opinions. A significant portion of this communication occurs within the app\'s group conversations. To facilitate the analysis of WhatsApp conversations, a web application called the WhatsApp Conversation Analyzer has been developed. This application leverages various Python libraries, including matplotlib, re, seaborn, streamlit, pandas, as well as foundational knowledge of NLTK (Natural Language Toolkit) for a comprehensive understanding. By importing a chat file of whatsapp from a group or single user, this hybrid system combines NLP (Natural Language Processing), NLTK, and machine learning techniques to perform a detailed analysis and provide valuable insights
I. INTRODUCTION
We presented a WhatsApp Data Sentiment Analyzer in this study. WhatsApp chat data contains several sorts of messages between group members and individual users. The exported conversation file can be used to train machine learning and natural language processing models. These technologies create the best learning environment. This application analyzes data extracted from exported WhatsApp chats. The main benefit of this system is that it is built with simple Python libraries such as seaborn, streamlit, numpy, matplotlib, and pandas. These are frequently used to generate data frames and graphs.
The overall goal of this research project is to provide a comprehensive platform that integrates statistical analysis, dataset modification methods, and sentiment analysis to offer insightful information about WhatsApp discussions. Researchers and analysts may better understand user behavior and communication dynamics in the digital age by utilizing the tool's capabilities to find patterns, trends, and sentiment variations within the chat data.
II. LITERATURE REVIEW
This paper introduces VADER (Valence Aware Dictionary and sEntiment Reasoner), a rule-based model specifically designed for sentiment analysis of social media text. VADER utilizes a combination of lexical and grammatical heuristics, along with a pretrained sentiment lexicon that incorporates both valence scores and lexical features. The model is trained on human-annotated data and is shown to outperform several state-of-the-art baselines on sentiment classification tasks. VADER's design principles prioritize simplicity, efficiency, and generalizability, making it suitable for real-time applications and large-scale social media analysis[1]. A study was conducted on sentiment analysis of WhatsApp group chats using VADER and machine learning algorithms. The experimental results show that VADER performs well in sentiment analysis but lags in accuracy[2].
The effectiveness of VADER in analyzing the sentiment of WhatsApp Conversation was shown by the experimental results from the study conducted by Singh , Kumar and Joshi[3]. The study was conducted by Ullah, Hassan and Malik to determine the sentiment polarity and evaluate the performance of VADER in comparison to NAIVE BAYES. The experimental results demonstrate that VADER achieves higher accuracy and precision compared to Naive Bayes in sentiment analysis of WhatsApp messages[4].
The study was conducted to analyze the sentiment polarity of group chat messages and evaluate the performance of VADER in comparison to Random Forest. The experimental results demonstrate the effectiveness of VADER in sentiment analysis and highlight the strengths of Random Forest in handling complex sentiment patterns[5].
The study was conducted to analyze the sentiment polarity of the chat messages and evaluate the performance of VADER in comparison to KNN. The experimental results demonstrate the effectiveness of VADER in sentiment analysis and highlight the advantages of the KNN algorithm in capturing similar sentiment patterns[6]. The study presents the Senti-N-Gram approach, which is a lexicon-based method for sentiment analysis incorporating n-grams. Traditional lexicon-based approaches for sentiment analysis often overlook the context and combinations of words by considering individual words alone. The Senti-N- 3 Gram approach addresses this limitation by treating n-grams, contiguous sequences of n words, as the fundamental units for sentiment analysis[7].
III. METHODOLOGY
A tool for statistical analysis of WhatsApp talks is the WhatsApp Data Sentiment Analyzer. Working with exported conversation files will aid in producing various plots of analysis.The methodology for the WhatsApp data analyzer project involves several steps:
IV. SENTIMENT ANALYSIS USING VADER
The method of identifying whether a piece of writing is good, negative, or neutral is known as sentimental analysis. The Algorithm below is intended for use in financial texts. It consists of the following steps:
The model\'s primary goal is to analyse WhatsApp messages and present the results of such analyses. The model also employs the Vader implementation for sentiment analysis. Following completion, the system generates consistent outcomes. The system is completely simple to use, allowing even those with limited computer knowledge to run the generated system. The model performs well with small data sets and lexicons. Future work will be to make it work for n-grams as Vader only works for unigrams, which could be accomplished by utilising other machine learning models or adding a dictionary in Vader for n-gram tokens. Additionally, because Vader is faster than other sentiment analysis tools, the goal is to maintain speed and efficiency.
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Copyright © 2023 Nilanjan Jana, Priyesh Pandey, Prof. Shallu Bashambu. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Paper Id : IJRASET53596
Publish Date : 2023-06-02
ISSN : 2321-9653
Publisher Name : IJRASET
DOI Link : Click Here