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Sentiment analysis, also known as opinion mining, is a crucial application of Natural Language Processing (NLP) that aims to determine the emotional tone behind textual data. This project focuses on implementing sentiment analysis using Artificial Intelligence (AI) and Machine Learning (ML) techniques to automatically classify text data into positive, negative, or neutral sentiments. Leveraging supervised learning algorithms such as Logistic Regression, Support Vector Machines (SVM), and advanced deep learning models like LSTM and BERT, the system is trained on labeled datasets from social media, product reviews, and news articles. Key processes include data preprocessing, feature extraction using techniques like TF-IDF and word embeddings, followed by model training and evaluation using performance metrics such as accuracy, precision, recall, and F1-score. The project demonstrates how AI/ML techniques can effectively extract subjective information and provide valuable insights for businesses, governments, and individuals. The results show a high degree of accuracy in classifying sentiment, validating the effectiveness of the chosen models and methodologies.
Keywords:
Sentiment Analysis, Artificial Intelligence, Machine Learning, Natural Language Processing, Text Classification, Opinion Mining, Logistic Regression, SVM, LSTM
Cite Article:
"sentiment analasis using aiml", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2455-2631, Vol.10, Issue 7, page no.b418-b421, July-2025, Available :http://www.ijrti.org/papers/IJRTI2507161.pdf
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ISSN:
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator