In the areas of marketing and electronic advertising, Opinion Mining has a broader domain. The advertiser must analyze the performance / popularity of the advertisements he has posted on the site. The mechanism based on the star rating can be fraudulent, due to robots or automatic responders. Therefore, it is necessary to analyze the current entity system or products using reviews (comments). Opinion Mining refers to the extraction of those lines or phrases in the huge raw data that express an opinion. On the other hand Sentimental Analysis is the analysis of feelings identifies the polarity (sentiment) of the opinion that is extracted from the review. Today, social networking sites and online shopping sites are used by users to express their opinion on products, events, peoples etc. Many users that express their opinion regarding any entity/Product, there may be chances that reviews are not written in correct form (Dictionary). Because of reviews available on these sites may contain noise such as spelling errors, typographical errors, standard abbreviations, and elegant writing. It is necessary to make data noise-free so that it can be used for opinion extraction. This paper describe a framework that was proposed to conduct opinion analysis of noisy reviews using techniques such as calculate similarity of terms and frequency of the document. The reviews of different products have been tested by this framework and the corresponding result is shown in negative (-ve) and positive (+ve ) form. The results are satisfactory for all the tested products.

Opinion mining, Sentiment Analysis, Opinion extraction, Document Frequency

[1]. B. Pang, and L. Lee, “Opinion Mining and Sentiment Analysis,” Foundations and Trends in Information Retrieval, Volume. 2, 2008.
[2]. Anand Mahendran and Anjali Duraiswany, “Opinion Mining for text classification”, International Journal of Scientific Engineering and Technology , Volume No.2, 2013.
[3]. Deepali Virmani, Vikrant malhotra and Ridhi tyagi, ” Sentiment Analysis Using Collaborated Opinion Mining”, 2014.
[4]. B. Liu, “Sentiment Analysis and Subjectivity,” Handbook of Natural Language Processing, Second Edition, 2010.
[5]. M. Hu, and B. Liu, “Mining and Summarizing Customer Reviews”, KDD’ 04. USA, August 2004.
[6]. N. Jakob and I. Gurevych. Using anaphora resolution to improve opinion target identication in movie reviews. In Proceedings of the ACL 2010 Conference Short Papers, pages 263-268. Association for Computational Linguistics,2010.
[7]. J. S. Kessler and N. Nicolov. Targeting sentiment expressions through supervised ranking of linguistic configurations. In ICWSM. AAAI Press,2009.
[8]. Z. Toh and J. Su. Nlangp: supervised machine learning system for the classification of appearance categories and the extraction of opinion objectives. In the Proceedings of the 9th International SemEval Workshop.
[9]. M.Hu and B. Liu. Extraction and summary of customer reviews. In Proceedings of the 10th ACM SIGKDD International Conference on Discovering Knowledge and Data Mining, pages 168-177. ACM, 2004.
[10]. J. Jmal and R. Faiz. Customer review summary focus via twitter and sentwordnet. In Proceedings of the 3rd International Conference on Web Intelligence, Mining and Sementics , 2013.
[11]. L. Ferreira, N. Jakob and I. Gurevych. A comparative study of the characteristics extraction algorithms in customer reviews. In Semantic Computing, 2008 IEEE International Conference, in 2008.
[12]. A.M. Popescu and O. Etzion . “Extraction of product characteristics and review opinions. In the processing of natural language and text mining”, 2007.
[13]. Forman, B. Pang and L. Lee, “A Sentimental Education: Sentiment Analysis Using Subjectivity Summarization Based on Minimum Cuts,” Proc. 42nd Ann. Meeting on Assoc. for Computational Linguistics.
[14]. W. Jin and H.H. Ho, “A Novel Lexicalized HMM-Based Learning Framework for Web Opinion Mining,” Proc. 26th Ann. Int’l Conf. Machine Learning, pp. 465-472, 2009.
[15]. G. Qiu, B. Liu, J. Bu, and C. Chen, “Opinion Word Expansion and Target Extraction through Double Propagation,” Computational Linguistics, vol. 37, pp. 9-27, 2011.
[16]. B. Liu. Mining opinion and analysis of sentiment. In Web Data Mining, pages 459-526. Springer, 2011.
[17]. N. Jakob and I. Gurevych, “Extracting Opinion Targets in a Single and Cross-Domain Setting with Conditional Random Fields,” Proc. Conf. Empirical Methods in Natural Language Processing, pp. 1035-1045, 2010.
[18]. M. Hu, and B. Liu, “Mining and Summarizing Customer Reviews”, KDD’ 04. USA, August 2004.
[19]. Phani gadde, L.V. subramaniam, Tanveer A. Faruquie, “Adapting a WSJ trained Part-of-Speech tagger to Noisy Text” Joint Workshop on Multilingual OCR and Analytics for Noisy Unstructured Text Data (J-MOCR-AND), 17-September 2011.
[20]. P. Turney, “Thumbs Up. Thumbs Down, Semantic Orientation Applied To Unsupervised Classification of Reviews”, Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics (ACL), 2002.
[21]. L. Dey, and M. Haque, , “Opinion mining from noisy text data,” Springer IJDAR , 2009.
[22]. Shikha parashar , Dr.R.K. Gupta, “A Survey on “Citizen Opinion Mining Initiatives In Government Decision Making”, IJSART, ISSN 2395-1052, Volume -3, 7 –July 2017.