Opinionated Text Classification For Hindi Tweets Using Deep Learning
C P Sindhu, Shilpi Adak, Soumya Celina Tigga · 2021
The recent years have witnessed a significant growth in the data collected from the reviews posted on various websites. Reviews are a direct way of getting the response of the customers and clients of any business, making it a convenient way for getting feedback for marketing, performance and other such characteristics in association with any product or service. The opinions mined from these collections of data can provide strategies to improve the sales based on how well a product is received. This is done in two steps, first being the Subjectivity Detection followed by Sentiment Analysis. For this process, various methods have been already introduced in this field. These vary from SVMs, Naive-Bayesian, deep learning etc. Since, English is the most commonly used language in the world, it is not surprising that most work done in this field focuses on the same. But it is already known that there are roughly around 6500 languages used around the world. India alone has 447 languages which ranks it fourth on the list of countries with the greatest number of languages. The proposed research work focuses on sentiment classification in Hindi language text. The proposed research work has attempted to experiment with a method that does not rely on availability language dictionaries. This is done by creating a completely numerical data corresponding to the text. The model proposed in this paper will use a combination of Recurrent Neural Network and Convolutional Neural Network model to extract the subjective data form the given dataset of movie reviews.