Rule Based Morphological Variation Removable Stemming Algorithm
Maheswari S1, K. Arthi2
1S Maheswari, Assistant Professor in the Department of Computer Science, Bishop Heber College, Trichy.
2Dr. K. Arthi MCA.,M.Phil.,Ph.D, Assistant Professor in Department of Computer Applications, Government Arts College, Coimbatore.
Manuscript received on November 15, 2019. | Revised Manuscript received on November 23, 2019. | Manuscript published on November 30, 2019. | PP: 1809-1814 | Volume-8 Issue-4, November 2019. | Retrieval Number: C6200098319/2019©BEIESP | DOI: 10.35940/ijrte.C6200.118419
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Sentiment analysis is a technique to analyze the people opinion, attitude, sentiment and emotion towards any particular object. Sentiment analysis has the following steps to predict the opinion of a review sentences. The steps are preprocessing, feature selection, classification and sentiment prediction. Preprocessing is the main important step and it consists of many techniques. They are Stop word Removal, punctuation removal, conversion of numbers to number names. Stemming is another important preprocessing technique which is used to transform the words in text into their grammatical root form and is mainly used to improve the retrieval of the information from the internet. It is applied mainly to get strengthen the retrieval of the information. Many morphological languages have immense amount of morphological deviation in the words. It triggered vast challenges. Many algorithms exist with different techniques and has several drawbacks. The aim of this paper is to propose a rule based stemmer that is a truncating stemmer. The new stemming mechanism in this paper has brought about many morphological changes. The new rule based morphological variation removable stemming algorithm is better than the existing other algorithms such as New Porter, Paice/Lovins and Lancaster stemming algorithm.
Keywords: Preprocessing, Stemming, Index Compression Factor, Word stemmed Factor.
Scope of the Article: Web Algorithms.