News Headlines Sentiment Analysis Using Vectorization Techniques

Sentiment analysis, or opinion mining, is a robust method for interpreting emotions and perspectives within textual data. Its significance lies in providing accurate public sentiment analysis for headlines, offering valuable insights to businesses, media, and researchers. The process comprehensively examines news headlines, converting text into numerical representations for sentiment score predictions. It effectively captures the semantic meaning and contextual information in headlines using vectorization techniques like Count Vectorization and TF-IDF Vectorization. The identification of recurrent sentiment patterns across diverse news headlines is achieved through machine learning algorithms, which are assessed using standard metrics. This research contributes by developing sentiment analysis approaches, offering insights into popular opinions and patterns across various industries. The methodology discerns target labels indicating each headline's positive, negative, neutral, and compound scores. The sentiment column is then calculated based on the compound score. Training the model using NLP, our proposed Count Vectorizer method attains an accuracy score of 90.25%, surpassing the TF-IDF Vectorizer model with a slight decrease in accuracy.