Text vectorization in sentiment analysis: A comparative study of TF-IDF and Word2Vec from Amazon Fine Food Reviews - Lu Jiaxin (ITM Web of Conferences)
Sentiment analysis is a practical tool for marketing and branding teams. Companies can collect and analyze opinions or reviews from social media platforms, blog posts, and other numerous forums. It may help them acquire positive feedback to reinforce strengths or identify negative emotions to make improvements. The research is to compare two text vectorization methods in opinion mining: Term Frequency-Inverse Document Frequency (TF-IDF) and Word2Vec, using Amazon Fine Food Reviews dataset. This study will use these two methods to vectorize preprocessed text data and also input the vectorized data to the emotion classification model, analyzing the performance of two methods in the emotion classification task. The consequence indicates that the former outperforms the latter in handling large datasets, particularly in distinguishing between different sentiment categories, but latter is superior in capturing the semantic relationship of words. Therefore, it is suggested that the advantages of the two methods be combined in practical applications to improve the accuracy and efficiency.