With the explosive growth of user generated messages, twitter has become a social site where millions of users can exchange their opinions. Sentiment analysis on twitter data plays an important role in finding public opinions which have provided an economical and effective way timely, which is very useful for decision making in various domains. A company can take the public opinion in tweets to obtain user review towards its products where a politician can adjust his position with respect to the opinion change of the public. There have been a large number of research studies and industrial applications in the area of public sentiment tracking and modeling. Millions of users give their opinions on Twitter, making it a valuable platform for tracking and analyzing public sentiment. Such tracking and analysis can provide critical information for decision making in various domains. So, it has attracted attention in both academic and industry. Previous researches showed that the tweet was classified appropriately only if the tweet would contain the exact same label as the training set. But this approach fails when the tweet contains a synonym or a variant of the label instead of the exact same label.

Classifier, Opinion Mining, Lexicon, Sentiment Analysis, Twitter

[1] Chenliang Li, Aixin Sun, Jianshu Weng, and Qi He, Member IEEE, “Tweet Segmentation And Its Application To Named Entity Recognition”, IEEE Transactions on knowledge and data engineering ,Vol.27,No.2, pages. 558-570 , 2015.
[2] Ana C. E. S. Lima, Lean Dran. Castro, “Development of a Novel Algorithm for Sentiment Analysis Based on Adverb Adjective Noun
Combinations”, IEEE, 2012.
[3] R. Varghese, “A Survey on Sentiment Analysis and Opinion
Mining”, IJRET, 2013.
[4] Ana C. E. S. Lima, Lean Dran.Castro “Automatic sentiment Analysis
of Twitter Message”, IEEE, 2012.
[5] Magar Ranjeet, Bhoge Swapnil, “A Survey on Tweet Segmentation
and its Application to Named Entity Recognition”, IJIRCCE, 2016.
[6] Chenliang Li, Aixin Sun, Anwitaman Datta “ Twevent: Segment-
based Event Detection from Tweets”, ACM, 2012.
[7] M. Ganga, S. Aanjan Kumar,“Segmenting and Detecting Malicious
Tweets and Harmful Entity Recognition” , IJIRCCE, 2016.
[8] Hu X. Tang, “The How, When and Why of Sentiment Analysis”,
IJCTA, 2013.
[9] R. Gomathi, M. Rajkumar, “ Tweet Segmentation And
Classification For Rumor Identification using KNN Approach”,
IJCRME, 2016.
[10] Shachi H. Kumar, University of California, Santa Cruz Computer
Science, “Twitter Sentiment Analysis CMPS 242 Project Report”.
[11] Ian H. Witten, “Text Mining”, pp. 1-23.
[12]Vishal Gupta , Gurpreet S. Lehal, “A Survey of Text Mining
Techniques and Applications”, JOURNAL OF EMERGING
TECHNOLOGIES IN WEB INTELLIGENCE, Vol.1, 2012.
[13] Xiaowen Ding, Bing Liu, Philip S. Yu, “A Holistic Lexicon
Based Approach to Opinion Mining ”, ACM, 2008.
[14] Ion Smeureanu, Cristian Bucur, “Applying Supervised Opinion
Mining Techniques on Online User Reviews”, Informatics, Economics, Vol .16, 2012.
[15] K. Nathiya, Dr. N. K. Sakthivel, “Development of an Enhanced
Efficient Parallel Opinion Mining for Predicting the Performance
of Various Products”, International Journal of Innovative
Research in Computer and Communication Engineering, Vol.1,
2013.
[16] Arti Buche, Dr. M. B. Chandak, Akshay Zadgaonkar, “OPINION
MINING AND ANALYSIS: A SURVEY”, International Journal
on Natural Language Computing, Vol.2, 2013.
[17] Doyen Sahoo, Chenghao Liu, and Steven C.H. Hoi “ Malicious
URL Detection using Machine Learning: A Survey”, IEEE, 2017.
[18] V. Gayathri, A.E. Narayanan “Tweet Segmentation And
Classification For Rumor Identification Using KNN Approach”,
Indian J. SCI. Res. 14 (1): 102-108, 2016.
[19] S. Kukku S, Reshma Reghu and Gaina K.G, “Tweet Segmentation
and its Application Using RandomWalk And Part-of-speech
Methods”, I J C T, pp. 7497-7501 , 2016.
[20] MandhalaVinoothna, “Segmentation of Trust Worthy Based Secure
Data”, International Journal of Big Data Security Intelligence
Vol.2, No.2, pp. 23-28 ,2015.
[21] Chetan Chavan, Ranjeet Singh, “Summarization of Tweets and
Named Entity Recognition from Tweet Segmentation”,
International Conference on Automatic Control and Dynamic
Optimization (ICACDOT),International Institute of Information
Technology, pages. 66-71 , 2016.
[22] Sonam Meshram, Hirendra Hajare, “Tweet Segmentation and
Enhancement of Tweets”, International Journal of Science and
Research (IJSR), Volume.5 Issue.5, pages. 577-579, 2016.
[23] Prof. Vikas Balasaheb Burgute, Prof. A. K. Gupta, “Named Entity
Recognition using Tweet Segmentation ”, International Research
Journal of Engineering and Technology (IRJET), Vol: 4, Issue: 7,
pages. 1068-1075, 2017.