Term Frequency Inverse Document Frequency based Sentiment Analysis using Machine Learning Approaches

The sentiment analysis process can be of great help in performing post-processing tasks, such as analyzing a person’s opinion. The key goal of this research is to analyze the sentiment expressed by a particular sentence. The unstructured nature of textual data makes it necessary to parse text into logical structures so that machines can use it effectively. Furthermore, there are different linguistic forms that make it difficult to condense a natural language interpretation by matching sentences with similar semantic structures but different lexicography. Improved model accuracy can be achieved through the use of Term Frequency Inverse Document Frequency (TF-IDF) to quantify the importance of the word. In this study, we evaluate three different classifiers: the Support Vector Machine, the Naive Bayes, and the logistic Metrics such as accuracy, precision, and recall are used to assess regression and performance. These parameters are useful in measuring the efficiency of the algorithm in accurately classifying the sentiments existing in the data.