Semantic Orientation Based Decision Making Framework for Big Data Analysis of Sporadic News Events

Abstract

The growing public endorsement of social media has changed public life dramatically. Public views and suggestions have now become important for both organizations and individuals. Big data scientists and data mining analysts are increasingly moving their attention toward sentiment analysis because of the growing rate of user-generated contents over microblogging sites. Sentiment analysis is a research field related to computationally identifying public views, feelings, recommendations, opinions and sentiments about focused entities. Research literature shows traces of research work on product and movie reviews for better decision making using big data analysis. Big data analytics offer remarkable opportunities to individuals as well as organizations by providing proficient decision making frameworks and improved forecasting models. The sociopolitical collaboration has gained much attention from online users over the past few years. In this research we analyzed public views, sentiments and opinions shared on social media about a democratic participatory activity called Azadi-March, which was held in Pakistan with participation of online users from all over the world. We carried out computational semantic orientation on public tweets for analyzing public awareness and the effects of online communication through social media over the real world public decision making. We employed unsupervised approach for identification and scoring of tweets. We used lexicon based approach in which annotated lexica are used for scoring verbs, adverbs and other parts of speech. A corpus is used for scoring adjectives and informal opinion indicators. Emoji, exclamatory statements and other additional features are incorporated for supplementary analysis. We noticed that emoticons and NetLingo play significant role in sentiment orientation. Opinion groups are generated from all retrieved tweets and aggregate sentiment weights of opinion groups are computed. The findings of this study indicate that our proposed lexicon based approach outperforms the contemporary machine learning techniques by achieving 86% average accuracy at sentence level sentiment analysis.