This document discusses automatic sentiment analysis for unstructured data. It introduces sentiment analysis and how it is performed at the document, sentence, and entity levels. It then discusses related work involving machine learning techniques, natural language processing, and text mining approaches. The document proposes an approach that considers both subjective and objective sentences. It describes plans to test the approach on Indian political news articles using a sentiment dictionary and to evaluate the results based on precision, recall, and F-score.

1. Automatic Sentiment Analysis
for Unstructured Data
Ms. Sunayana R. Gawde
M. Tech Part I
14109

2.  Jalaj S. Modha
 Gayatri S. Pandi
 Sandip J. Modha
Authors

3.  International Journal of Advanced Research in
Computer Science and Software Engineering
 Volume 3, Issue 12, December 2013 with ISSN: 2277
128X
Published in..

4.  Big data
 Sentimental Analysis is to get the real voice of people
towards specific product, services, organization,
movies, news, events, issues and their attributes.
 Sentiment analysis is done on three levels
 Document Level
 Sentence Level
 Entity or Aspect Level
Introduction

5.  Sentiment Analysis for Subjective Sentences
 Positive sentiment in subjective sentences
“I like my new Dell Laptop”
 Negative sentiment in subjective sentences
“Krish 3 is a flop movie”
 Neutral sentiment in subjective sentences
“I’m going for a long drive”
 Sentiment Analysis for Objective Sentences
Related Work

6.  Machine Learning Techniques
 Supervised Machine Learning Techniques
 Unsupervised Machine Learning Techniques
 Natural Language Processing
 Beg of words, Hidden markov model, part of speech
(POS), N-gram algorithms and parsing techniques
 Text Mining Techniques
EXISTING TECHNIQUES AND
APPROACHES

7.  Traditional approach:
Consider only subjective sentences and ignore
objective sentences.
 Proposed approach:
PROPOSED APPROACH

8. Overview

9.  Indian political news articles.
 Prepare the SentiWord dictionary which contains
terms and threshold values for opinionated words
 Results will be evaluated using Precision, Recall and F-
Score.
Experimental Work

10.  Creation of Global SentiWord Dictionary with
maximum Coverage
Future Work

11.  [1] Bing Liu, Sentiment Analysis and Opinion Mining,
Morgan and Claypool Publishers, May 2012.p.18-19,27-
28,4445,47,90-101.
 [2] Ronen Feldman, James Sanger, The Text Mining
Handbook-Advance Approaches in Analysing
Unstructured Data, Cambridge University Press,2007.
 [3] Ronen Feldman, “Techniques and Application of
Sentiment Analysis”, Communication of ACM, April
2013, vol. 56.No.4.
REFERENCES

12. THANK YOU!