Rule Based Sentiment Analysis of Punjabi Tweets Using Vector Evaluation Method

Abstract

Due to the exponential enhancement in the Internet usage and replacement of public opinions, Sentiment Analysis becomes an important process in today’s life. Sentiment Analysis is a process of extracting information from opinions generated by the users. Twitter is a micro-blogging platform which provides a tremendous amount of data which can be used for various applications of Sentiment Analysis like predictions, reviews, elections, marketing, etc. The thoughts or opinions of other people provide information that helps in decision making process. But, Sentiment Analysis is a challenging task because it is very difficult to find the exact sentiment from text as there are so many challenges like entity identification, subjectivity detection in performing sentiment analysis. The project work carried out in the dissertation is focused on Rule based sentiment analysis. Python is simple yet powerful, high-level, interpreted and dynamic programming language, which is well known for its functionality of processing natural language data by using NLTK (Natural Language Toolkit). The goal of this dissertation is to classify twitter data into sentiments (News, Personal, Opinions and Entertainment) by calculating weight of each word and the words with highest weights are selected as features which will be then compared to the rest of the words in order to find the most suitable category for Punjabi Tweets.