Word Cloud for Online Mobile Phone Tweets towards Sentiment Analysis
Naramula Venkatesh1, A.Kalaivani2

1Naramula Venkatesh , Assistant Professor, Vignana Bharathi Institute of Technology(VBIT), Ghatkesar Hyderabad (Telangana), India.
2A. Kalaivani, Associate Professor, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Science,(SIMATS).chennai, India.
Manuscript received on July 20, 2019. | Revised Manuscript received on August 10, 2019. | Manuscript published on August 30, 2019. | PP: 683-689 | Volume-8 Issue-6, August 2019. | Retrieval Number: F7964088619/2019©BEIESP | DOI: 10.35940/ijeat.F7964.088619
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: People became more eager to express and share their opinions on web regarding day-to-day activities or global issues. Social media contributed a transparent platform to share views across the world. Recently research communities, academia, public and service industries are working rigorously on sentiment analysis also termed as opinion mining, to extract and analyze public mood and views. Data pre-processing is a crucial step in sentiment analysis and selecting an appropriate pre-processing methods can improve classification accuracy. In this paper, we explore the role text pre-processing of online mobile phone reviews towards Sentiment Analysis. Proposed text pre-processing methods remove inconsistent and redundant elements on the collected data to improve classification accuracy. Proposed Pre-processing methods involves removal of punctuations, special characters, digits, escaping HTML characters, decoding data, Apostrophe Lookup, Removal of Stop-words, Removal of URLs, Removal of Expressions. The final pre-processed online review data are presented in the form of word cloud with the frequency statistics of the keywords.
Keywords: Text Pre-Processing, Sentiment analysis, Word Cloud, Online Mobile Phone Reviews, Opinion mining, Accuracy.