An efficient Decision Tree Algorithm for analyzing the Twitter Sentiment Analysis
Dr.A.Nisha Jebaseeli S.Kasthuri · Journal of Critical Reviews · 2020
Opinion mining and sentiment analysis are valuable to extract the useful subjective information out of text documents. The huge amount of information from this medium has become an attractive resource for organizations to monitor the opinions of users, and therefore, it is receiving a lot of attention in the field of sentiment analysis. However, performing sentiment analysis is a challenging task for the researchers in order to find the users sentiments from the large datasets, because of its unstructured nature, slangs, misspells and abbreviations. To address this problem, a new proposed system is developed in this research study. Here, the proposed system comprises of four major phases; data collection, pre-processing, key word extraction, and classification. Initially, the input data were collected from the twitter dataset. After collecting the data, pre-processing was carried-out for enhancing the quality of collected data. The pre-processing phase comprises of two systems; lemmatization, and removal of stop-words and URLs. Then, an effective topic modelling approach Latent Dirichlet Allocation (LDA) was applied to extract the keywords and also helps in identifying the concerned topics. The extracted key-words were classified into three forms (positive, negative and neutral) by applying an effective machine learning classifier: Decision Tree (DT). The experimental outcome showed that the proposed system enhanced the accuracy in sentiment analysis up to 6-20% related to the existing systems.