• Abhishek Gupta
• Vandana Bhattacharjee
• Aparna U.

In the contemporary digital age, sentiment analysis has emerged as a crucial area of research and application owing to the exponential growth of textual data generated across various online platforms such as social media, customer reviews, and news articles. Understanding sentiment from text is pivotal for businesses to gauge customer satisfaction, monitor public opinion, and make data-driven decisions. This research aims to develop a sentiment analysis model using machine learning techniques to classify text data into positive, negative, or neutral sentiments, thereby contributing to the field of natural language processing and information retrieval. The methodology employed in this research involves several key steps. First, a dataset comprising labeled text samples is collected and preprocessed to remove noise and standardize the text format. Next, a feature extraction technique such as TF-IDF (Term Frequency-Inverse Document Frequency) or word embeddings is applied to represent text data numerically. Subsequently, machine learning algorithms like Support Vector Machines (SVM), Naive Bayes, or deep learning models such as LSTM (Long Short-Term Memory) are employed to train and evaluate the sentiment classification model. The results of this sentiment analysis demonstrate promising accuracy in sentiment classification across different domains of text data. By leveraging machine learning algorithms and textual feature representations, the developed model achieves robust performance in distinguishing sentiments expressed in text. The significance of this work lies in its practical implications for businesses, market researchers, and social media analysts, enabling them to automate sentiment analysis tasks and derive valuable insights from large volumes of text data efficiently. In conclusion, this research underscores the effectiveness of machine learning techniques in sentiment analysis, showcasing their potential for real-world applications in sentiment monitoring and opinion mining. The sentiment analysis model developed serves as a valuable tool for extracting sentiment from textual data, aiding decision-making processes in diverse domains. The implementation of this research utilizes Python programming language along with popular libraries such as scikit-learn and TensorFlow for machine learning and natural language processing tasks.

Sentiment,tweets,positive,negative

"Sentiment Analysis Using Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 6, page no.e54-e60, June-2024, Available :http://www.jetir.org/papers/JETIR2406411.pdf

"Sentiment Analysis Using Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 6, page no. ppe54-e60, June-2024, Available at : http://www.jetir.org/papers/JETIR2406411.pdf