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Comparative Study of Available Technique for Detection in Sentiment Analysis
Parameter of Sentiment Analysis NLP Approach ML Approach
Keyword Selection Not Efficient. Most Effective.
Sentiment is Domain Specific Efficient to check grammar in
specific sentiment statement.
Not more effective and require
specific training to statement.
Multiple Opinions in a Sentence Not efficient for frequently
changing opinion.
Efficient for differ opinion
statement
by using ML agent.
Negation Handling More efficient for negation
statement.
Equally Efficient for negation
statement.
Table I Comparative Study Of Nlp And Ml Approach.
In this paper , two different approaches are considered and compared with the help of different
parameters so from this table it can be noticed that NLP approach is much efficient in keyword selection ,
efficient to check grammar in specific sentiment statement , not efficient for frequently changing opinion , more
efficient for negation statement and for ML approach it has noticed that it is more efficient in keyword
selection, not more effective in domain specific sentiment and require specific training to statement, efficient
for differ opinion statement by using ML agent and equally efficient for negation statement. So therefore if
there is combination of two approaches then analysis of sentiment will be more effective.
VI. CONCLUSION
Sentiment Analyzer (SA) consistently demonstrated high quality results of for the general web pages.
Although some amount of human expert involvement may be inevitable in the validation to handle the
semantics accurately, plan on more research on increasing the level of automation. Nonetheless, the synset and
sentiment lexicons, used are better suited to more formal styles of writing. An alternative approach is to replace
our synsets and lexicons with “slang” versions or even the automatic generation of sentiment lexicons on a slang
corpus. Another area of interest is the difficulty in correlating topics with sentiment. Intuition says that topics
themselves should portray different sentiments, and so should be useful for sentiment analysis. This method
turns out to be fairly crude, as sometimes topics may be too neutral or too general. Thus, it is concluded that
hybrid approach that is combination of NLP and ML approach can strengthen analysis of sentiment or opinions
on different parameters and can give a better result than applying individual approach .
REFERENCES
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[3] B. Pang, L. Lee, and S. Vaithyanathan. Thumbs up? Sentiment classification using machine learning techniques. In Proc. of the
2002 ACL EMNLP Conf., pages 79–86, 2002.
[4] S. Morinaga, K. Yamanishi, K. Teteishi, and T. Fukushima. Mining product reputations on the web. In Proc. of the 8th
ACM SIGKDD Conf., 2002.
[5] Jeonghee Yi, Tetsuya Nasukawa, Razvan Bunescu, Wayne Niblack,”Sentiment Analyzer: Extracting Sentiments about a Given
Topic using Natural Language Processing Techniques”, Proceedings of the Third IEEE International Conference on Data
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[6] Raymond Hsu, Bozhi See, Alan Wu,’’Machine Learning for Sentiment Analysis on the Experience Project”, 2010.
[7] Akshi Kumar ,Teeja Mary Sebastian, Sentiment Analysis: “A Perspective on its Past, Present and Future”,2012 .
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Author’s Profile:
Miss. Siddhi S. Patni is doing M.E (CSE) from G.H Raisoni College of Engineering and
Management, Amravati and has done B.E in Information Technology from SGBAU,
Amravati.
Prof. Avinash P. Wadhe: Received the B.E and from SGBAU Amravati university and
M-Tech (CSE) From G.H Raisoni College of Engineering, Nagpur (an Autonomous
Institute). He currently an Assistant Professor with the G.H Raisoni College of
Engineering and Management, Amravati SGBAU Amravati University. His research
interest include Network Security, Data mining and Fuzzy system .He has contributmore
than 20 research paper. He had awarded with young investigator award in international
conference.
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