SCANCPECLENS: A Framework for Automatic Lexicon Generation and Sentiment Analysis of Micro Blogging Data on China Pakistan Economic Corridor

• Main Authors: Bibi Amina, Tayyaba Azim
• Format: Article
• Language: English
• Published: IEEE 2019-01-01
• Series: IEEE Access
• Subjects: China Pakistan economic corridor (CPEC); <italic xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">k</italic>- nearest neighbor; logistic regression; lexicon generation; machine learning; natural language processing
• Online Access: https://ieeexplore.ieee.org/document/8831392/

With the growing availability of internet and opinion rich resources such as social networks and personal blogs, the task of mining public opinion and exploring facts has become more popular than ever before during the last decade. The latest trend has deeply transformed the way the governments interact with their citizens and offer them various services through continuous public engagement. The proposed framework SCANCPECLENS is an initiative to support performance assessment framework for e-government in Pakistan. The research takes into account the opinion of masses on one of the most crucial and widely discussed development projects, China Pakistan Economic Corridor (CPEC), considered as a game changer due to its promise of bringing economic prosperity to the region. The proposed framework suggests to use machine learning algorithms to automatically discover the public sentiment from microblogs on the matter nationally as well as internationally. We also present an automated way to create sentiment lexicon of positive, negative and neutral words on the subject. To the best of our knowledge, this theme has not been explored for opinion mining before and helps one in effectively assessing public satisfaction over government's policies in the CPEC region. The research is an initiative to discover new avenues of future research and direction for the government, policy making institutions and other stake holders and demonstrates the power of text mining as an effective tool to extract business value from vast amount of social media data.