Sentiment analysis for marathi language


In todays world opinions and reviews accessible to us are one of the most critical factors in formulating our views and influencing the success of a brand product or service. With the advent and growth of social media in the world stakeholders often take to expressing their opinions on popular social media namely Twitter. While Twitter data is extremely informative it presents a challenge for analysis because of its humongous and disorganized nature. This paper is a thorough effort to dive into the novel domain of performing sentiment analysis of peoples opinions regarding top colleges in India. Besides taking additional preprocessing measures like the expansion of net lingo and removal of duplicate tweets a probabilistic model based on Bayes theorem was used for spelling correction which is overlooked in other research studies. This paper also highlights a comparison between the results obtained by exploiting the following machine learning algorithms: Naïve Bayes and Support Vector Machine and an Artificial Neural Network model: Multilayer Perceptron. Furthermore a contrast has been presented between four different kernels of SVM: RBF linear polynomial and sigmoid



Natural language processing-DNN-SVM-Random Forest-SVM


machine learning


₹12000 (INR)


IEEE 2016