Evaluating Machine Learning Algorithms for New Indian Parliament Building Sentiment Analysis

The Indian Parliament is a great example of the nation's democratic ideals. The parliament with its upper (Rajya Sabha) and lower house (Lok Sabha) forms the highest law-making body of the nation. The old parliament building, constructed during British era, has structural issues and is insufficient to accommodate the increasing number of members of parliament. Thus, the ruling government of India decided to construct the new building with superior infrastructure which can accommodate the increasing number of members of parliament, architecture and Indian sculptures. The construction of new building led to controversies from various stakeholders of the country and neighbouring countries. The mixed opinions about the construction of new parliament building generated chaos leading to environmentalists, and conservationists filing case at Supreme Court of India to pause the construction of new building. Thus, it is essential to study the various sentiments of the civilians of India and develop a model with machine learning techniques that could classify the sentiments into positive, negative and neutral. To fill up this gap, authors in this research have generated the dataset, namely, New Indian Parliament Dataset-URL (NIPD-U) and New Indian Parliament Dataset-Paragraph (NIPD-P) from authentic web sources related to India’s new parliament building, and have developed machine learning models with five classifiers to classify the text into positive and negative sentiments. It was observed that for URL-based classification Naïve Bayes (NB) classifier outperformed other classifiers with 91% of accuracy. Further, paragraph-wise results showed that Random Forest (RF) showed highest accuracy of 92.34%.