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  Sentiment Analysis-Towards Harvesting Opinions from the Net  
  Authors : Ashwini Rao
  Cite as: ijcsn.org/IJCSN-2013/2-5/IJCSN-2013-2-5-07.pdf


Sentiment analysis also called as Opinion mining classifies various opinions in text into categories like positive, negative as well as an implicit category of neutral. The data for this classification comes from Web (reviews, blogs, social network, discussion forums etc.). This user generated content is now regarded as a true source for exploring factual and subjective information. Sentiment analysis application involves competitive and marketing analysis as well as detection of unfavorable rumors for risk management, thereby helping companies to improve customer service, enhance their products and check the vulnerability of competitors. Opinions which are classified as positive often mean profits and fame for individuals and customers but, the system unfortunately has a loop hole where fake opinions or reviews are posted to discredit some individuals or products without disclosing their true identity. The accuracy of a sentiment analysis system in principle is to find out how well it agrees with human judgment. This paper presents a survey of various Challenges, Data Store and Levels that appear in the field of sentiment analysis.


Published In : IJCSN Journal Volume 2, Issue 5

Date of Publication : 01 October 2013

Pages : 01 - 04

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Publication Link : ijcsn.org/IJCSN-2013/2-5/IJCSN-2013-2-5-07.pdf




Ms. Ashwini Rao : is pursuing PhD in Computer Engineering from NMIMS University. She has done her ME (Master of Engineering) and BE (Bachelor of Engineering) degrees in Computer Science. She is currently working as a Assistant Professor in Dept. of Information Technology at Mukesh Patel School of Technology Management and Engineering, Mumbai, India. She is currently working as a research scholar in the field of Sentiment Analysis.








Sentiment Analysis

Opinion Holder

Web 2.0

Supervised Learning

Spammer group detection

Subjectivity Classification

Machine Learning




The proliferation of various micro blogging sites offers an unprecedented opportunity to create and employ theories & technologies that search and mine for sentiments. The research community has been focusing on various mining aspects but reported solutions are still far from perfect. Most business intelligence solutions based on Sentiment analysis are extremely powerful but require a base knowledge of the underlying data in order to leverage them effectively.

Feature extractions and synonym grouping remain to be very challenging. The main question that crops up is the “sentiment analysis accuracy of the current state of the art algorithms”. This question is difficult to answer as there are so many individual sub problems which do not have annotated data for benchmark testing. One point that is worth mentioning is about the applications that Sentiment analysis can be used but requires to work on data about people’s preferences which may trigger concerns about privacy violations.

Sentiment-analysis technologies allow users to consult many people who are unknown to them, but this means precisely that it is harder for users to evaluate the trustworthiness of those people they are consulting. Thus, opinion-mining systems might potentially make it easier for users to be mis-led by malicious entities, a problem on which more research can be carried on. However, the huge practical need for such opinions will keep this field of sentiment analysis vibrant and lively for years to come.



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