Sentimental Analysis on Text data by using Unsupervised Methods
B. Manjula Josephine1, KVSN Rama Rao2, K.Ruth Ramya3, P.Sandeepa4, G.Yeshwanth5
1B.Manjula Josephine*, Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Guntur, (Andhra Pradesh), India.
2KVSN Rama Rao, Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Aziz Nagar, Hyderabad, India.
3K.Ruth Ramya , Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Guntur, (Andhra Pradesh), India.
4P.Sandeepa, Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Guntur, (Andhra Pradesh), India.
5G. Yeshwanth, Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Guntur, (Andhra Pradesh), India.
Manuscript received on September 17, 2019. | Revised Manuscript received on October 15, 2019. | Manuscript published on October 30, 2019. | PP: 843-847 | Volume-9 Issue-1, October 2019 | Retrieval Number: A9385109119/2019©BEIESP | DOI: 10.35940/ijeat.A9385.109119
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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: On the internet we can see how efficiently display the reviews by the user who brought the product so that it covers all the important points instead of just displaying few top comments or threads. The main agenda of the tool is to build and analyse all the reviews given by each customer and display the best product reviews for any app or product. As we all read reviews before we buy any product from any e-commerce or while installing any app but the major problem we face is there are huge number of reviews and most of the reviews we get is the top most review or a combination of bad and good review based on rating which sometimes may or may not tell the perks or cons of using the product so we tried to build a tool that analyse all the reviews and picks the best reviews which totally describe the product flaws defects or advantages. so for that purpose we are implementing the k-means clustering algorithm and in previous papers they have used RASP (robustical and accurate statistical parser)grammatical tagger to identify all kinds of nouns, adjectives, pronouns and etc. together. Here in our paper we are using k-means clustering algorithm which divides all kinds of identifiers based on the comments so that it gives a clear idea about the product and is given in graphical representation
Keywords: k-means, Sentimental Analysis, Emotion Detection.